Information processing system, information processing apparatus, information processing method, and recording medium

ABSTRACT

An information processing system including: a storage section that stores information about a plurality of agents capable of dialogue with a user, each agent having different attributes; a communication section that receives a message from the user from a client terminal, and also replies to the client terminal with a response message; and a control section that executes control to select a specific agent from the plurality of agents, according to an instruction from the user, record attributes of the specific agent updated according to dialogue between the specific agent and the user as the attributes of a user agent, specify a partner user who most resembles the attributes of the user agent by comparing the attributes of the user agent and attributes of a plurality of actually existing partner users, and notify the user of the existence of the partner user at a predetermined timing.

TECHNICAL FIELD

The present disclosure relates to an information processing system, aninformation processing apparatus, an information processing method, anda recording medium.

BACKGROUND ART

In recent years, advances in communication technologies have led to thefrequent exchange of messages over networks. Using an informationprocessing terminal such as a smartphone, a mobile phone terminal, or atablet terminal, a user is able to check messages transmitted from otherterminals, and transmit messages.

Also, an agent system that automatically responds to user messages on aninformation processing terminal has been proposed. Regarding such asystem, for example, Patent Literature 1 below describes a system inwhich a personal agent for a viewer resides in a terminal of the viewer,and through daily conversation with the viewer, the agent acquires theability to selection information preferred by the viewer.

Also, Patent Literature 2 below describes an interactive operationassistance system in which the generation of a representation and abehavioral pattern of an assistant is executed on the basis of pastconversations between a user and the assistant (a user interfacerealized by the animation of a character), a history other exchanges,personality/emotion and learned data based on the history, and the like.

Also, Patent Literature 3 below describes a system that selects anddisplays first impression attributes of an online search result, such asan online dating search. Also described is a system in which latentcandidates having the preferences sought by an individual are searchedfor in a dating service (romantic matchmaking service).

Also, Patent Literature 4 below describes a matching system in whichphoto data of male members and female members are stored, each memberviews the photo data of members of the opposite sex, and an ideal imageis presented on the basis of brain waves with respect to photos of theopposite sex.

CITATION LIST Patent Literature

Patent Literature 1: JP 2001-76002A

Patent Literature 2: JP 2002-41276A

Patent Literature 3: JP 2010-510577T

Patent Literature 4: JP 2013-539128T

DISCLOSURE OF INVENTION Technical Problem

Herein, the agent systems of the related art have been proposed asentertainment or as practical tools having entertainment qualities, suchas an agent that takes the place of a human being to become aconversation partner with a user, or to help a user with schedulemanagement and information organization. Also, it has been possible tochoose an agent from among multiple agents, and also to make the agentlearn and grow in response to conversation content.

However, the agent systems of the related art are merely automaticresponses by machines that imitate human beings, and communicating withthe agent does not lead to a real human being.

Accordingly, the present disclosure proposes an information processingsystem, an information processing apparatus, an information processingmethod, and a recording medium capable of connecting dialogue with anagent seamlessly to communication with a person in the real world.

Solution to Problem

According to the present disclosure, there is proposed an informationprocessing system including: a storage section that stores informationabout a plurality of agents capable of dialogue with a user, each agenthaving different attributes; a communication section that receives amessage from the user from a client terminal, and also replies to theclient terminal with a response message; and a control section thatexecutes control to select a specific agent from the plurality ofagents, according to an instruction from the user, record attributes ofthe specific agent updated according to dialogue between the specificagent and the user as the attributes of a user agent, specify a partneruser who most resembles the attributes of the user agent by comparingthe attributes of the user agent and attributes of a plurality ofactually existing partner users, and notify the user of the existence ofthe partner user at a predetermined timing.

According to the present disclosure, there is proposed an informationprocessing apparatus including: a communication section that transmits amessage from a user to a server apparatus storing information about aplurality of agents capable of dialogue with the user, each agent havingdifferent attributes, and also receives a response message with respectto the message; and a control section that executes control to select aspecific agent from the plurality of agents, according to an instructionby the user, and receive, from the server apparatus through thecommunication section at a predetermined timing, a notificationindicating an existence of an actually existing partner user who mostresembles the attributes of a user agent obtained by updating theattributes of the specific agent according to dialogue between thespecific agent and the user.

According to the present disclosure, there is proposed an informationprocessing method, executed by a processor, including: storing, in astorage section, information about a plurality of agents capable ofdialogue with a user, each agent having different attributes; receivinga message from the user from a client terminal, and also replying by acommunication section to the client terminal with a response message;and executing control to select a specific agent from the plurality ofagents, according to an instruction from the user, record attributes ofthe specific agent updated according to dialogue between the specificagent and the user as the attributes of a user agent, specify a partneruser who most resembles the attributes of the user agent by comparingthe attributes of the user agent and attributes of a plurality ofactually existing partner users, and notify the user of the existence ofthe partner user at a predetermined timing.

According to the present disclosure, there is proposed a program causinga computer to function as: a communication section that transmits amessage from a user to a server apparatus storing information about aplurality of agents capable of dialogue with the user, each agent havingdifferent attributes, and also receives a response message with respectto the message; and a control section that executes control to select aspecific agent from the plurality of agents, according to an instructionby the user, and receive, from the server apparatus through thecommunication section at a predetermined timing, a notificationindicating an existence of an actually existing partner user who mostresembles the attributes of a user agent obtained by updating theattributes of the specific agent according to dialogue between thespecific agent and the user.

Advantageous Effects of Invention

According to the present disclosure as described above, it becomespossible to connect dialogue with an agent seamlessly to communicationwith a person in the real world.

Note that the effects described above are not necessarily limitative.With or in the place of the above effects, there may be achieved any oneof the effects described in this specification or other effects that maybe grasped from this specification.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram explaining a summary of a communication controlsystem according to an embodiment of the present disclosure.

FIG. 2 is a diagram illustrating the overall configuration of thecommunication control system according to the present embodiment.

FIG. 3 is a block diagram illustrating an example of a configuration ofa speech agent server according to the present embodiment.

FIG. 4 is a diagram illustrating an exemplary configuration of adialogue processing section according to the present embodiment.

FIG. 5 is a flowchart illustrating a process of generating aconversation DB according to the present embodiment.

FIG. 6 is a flowchart illustrating a process of generating a phoneme DBaccording to the present embodiment.

FIG. 7 is a flowchart illustrating a dialogue control process accordingto the present embodiment.

FIG. 8 is a diagram explaining an exemplary data configuration of aconversation DB according to the present embodiment.

FIG. 9 is a flowchart illustrating a process of updating a conversationDB according to the present embodiment.

FIG. 10 is a flowchart illustrating a process of moving conversationdata from a personalized layer to a common layer according to thepresent embodiment.

FIG. 11 is a diagram explaining the moving of conversation data to abasic dialogue conversation DB according to the present embodiment.

FIG. 12 is a flowchart illustrating a process of moving conversationdata to the basic dialogue DB according to the present embodiment.

FIG. 13 is a diagram illustrating an example of advertisementinformation registered in an advertisement DB according to the presentembodiment.

FIG. 14 is a flowchart illustrating a process of inserting advertisementcontent according to the present embodiment.

FIG. 15 is a diagram illustrating an exemplary system configuration ofthe communication control system according to the present embodiment.

FIG. 16 is a diagram illustrating an exemplary configuration of adialogue processing section according to the present embodiment.

FIG. 17 is a diagram illustrating an exemplary configuration of amatching section according to the present embodiment.

FIG. 18 is a block diagram illustrating an example of a configuration ofa client terminal according to the present embodiment.

FIG. 19 is a flowchart illustrating an operating process in a searchphase according to the present embodiment.

FIG. 20 is a flowchart illustrating an operating process in a fixingphase according to the present embodiment.

FIG. 21 is a flowchart illustrating an operating process in anintroduction phase according to the present embodiment.

FIG. 22 is a flowchart illustrating an operating process in a supportphase according to the present embodiment.

FIG. 23 is a flowchart illustrating an operating process in apreparation phase according to an example.

FIG. 24A is a diagram illustrating a specific example of basicinformation according to the example.

FIG. 24B is a diagram illustrating a specific example of basic conditioninformation according to the example.

FIG. 25 is a diagram illustrating a user preference information exampleaccording to the example.

FIG. 26A is a diagram illustrating an agent basic information exampleaccording to the example.

FIG. 26B is a diagram illustrating an agent basic condition informationexample according to the example.

FIG. 27 is a diagram illustrating an agent preference informationexample according to the example.

FIG. 28 is a diagram illustrating an agent selection screen exampleaccording to the example.

FIG. 29A is a flowchart illustrating an operating process in a searchphase according to the example.

FIG. 29B is a flowchart illustrating an operating process in a searchphase according to the example.

FIG. 30 is a diagram explaining exemplary utterances of an agentutilizing a user activity history according to the example.

FIG. 31 is a diagram explaining exemplary utterances of an agentutilizing biological information about a user according to the example.

FIG. 32 is a diagram explaining exemplary utterances of an agentutilizing SNS information according to the example.

FIG. 33A is a flowchart of an agent dialogue process according to theexample.

FIG. 33B is a flowchart of an agent dialogue process according to theexample.

FIG. 34 is a flowchart illustrating an operating process of agentlearning according to the example.

FIG. 35 is a diagram for explaining a negativity/positivitydetermination and a phrasing determination according to the example.

FIG. 36 is a flowchart illustrating an operating process of matchingaccording to the example.

FIG. 37 is a diagram explaining a search using agent basic conditioninformation according to the example.

FIG. 38 is a diagram explaining a similarity computation of preferenceinformation according to the example.

FIG. 39 is a flowchart illustrating an operating process in a fixingphase according to the example.

FIG. 40 is a diagram illustrating a dialogue example in a case in whichagent preferences change according to the example.

FIG. 41 is a diagram illustrating an example of a notification screen ofan introduction approval request according to the example.

FIG. 42 is a flowchart illustrating an operating process in anintroduction phase according to the example.

FIG. 43 is a diagram illustrating an example of a notification ofintroduction to a user according to the example.

FIG. 44 is a diagram illustrating an example of a detailed informationdisplay screen of an introduced partner according to the example.

FIG. 45 is a flowchart illustrating an operating process in a supportphase according to the example.

FIG. 46 is a diagram illustrating an example of a meeting plan selectionscreen according to the example.

FIG. 47 is a diagram illustrating an example of a meeting plannotification screen according to the example.

FIG. 48 is a diagram illustrating an example of a final congratulatorymessage screen by an agent according to the example.

MODE(S) FOR CARRYING OUT THE INVENTION

Hereinafter, (a) preferred embodiment(s) of the present disclosure willbe described in detail with reference to the appended drawings. Notethat, in this specification and the appended drawings, structuralelements that have substantially the same function and structure aredenoted with the same reference numerals, and repeated explanation ofthese structural elements is omitted.

Hereinafter, the description will proceed in the following order.

1. Summary of communication control system according to embodiment ofpresent disclosure

2. Configuration

-   -   2-1. System configuration    -   2-2. Server configuration

3. System operating processes

-   -   3-1. Conversation data registration process    -   3-2. Phoneme DB generation process    -   3-3. Dialogue control process    -   3-4. Conversation DB update process    -   3-5. Advertisement insertion process

4. Dialogue control process

-   -   4-1. Configuration    -   4-2. Operating processes    -   4-3. Example        -   (4-3-1. Preparation phase)        -   (4-3-2. Search phase)        -   (4-3-3. Fixing phase)        -   (4-3-4. Introduction phase)        -   (4-3-5. Support phase)

5. Conclusion

<<1. Summary of Communication Control System According to Embodiment ofPresent Disclosure>>

The communication control system (agent system) according to anembodiment of the present disclosure makes it possible to connectdialogue with an agent seamlessly to communication with a person in thereal world. Hereinafter, a summary of the communication control systemaccording to the present embodiment will be described with reference toFIG. 1.

FIG. 1 is a diagram explaining a summary of the communication controlsystem according to an embodiment of the present disclosure. The userengages in everyday dialogue with an agent having a personality, and mayenjoy a variety of agent services depending on the situation, such asproviding recommendations about the real world, content on the Internet,or the like, providing information such as news, weather forecasts, andthe like, providing games, giving directions, and the like. Dialoguewith the agent is executed through the display section 106, for example,and an image of the agent and conversation may be displayed on thedisplay section 106. Also, agent speech is played back from a speaker(not illustrated). User utterances are collected by a microphone (notillustrated), and subjected to language analysis on the agent systemside.

Also, in the present embodiment, multiple agent characters, each havinga different personality, are prepared as the agent, and the user selectsand purchases an arbitrary agent character to utilize the agent servicethrough the agent character.

(Background)

Herein, the agent systems of the related art have been proposed asentertainment or as practical tools having entertainment qualities, suchas an agent that takes the place of a human being to become aconversation partner with a user, or to help a user with schedulemanagement and information organization. Also, it has been possible tochoose an agent from among multiple agents, and also to make the agentlearn and grow in response to conversation content.

However, the agent systems of the related art are merely automaticresponses by machines that imitate human beings, and communicating withthe agent does not lead to a real human being.

Accordingly, the present disclosure proposes an agent system directed atthe real world in which the system matches, according to dialoguebetween a user and an agent, a real person having a personality andpreferences similar to the agent, and seamlessly connects tocommunication and a meeting with the real person. By connecting with notonly a virtual person (agent), but also a real person, it is possible togive the user an enriched lifestyle and sense of emotional satisfactionin the original human sense.

In addition, the communication control system according to the presentembodiment is not limited to a speech agent that responds by speech, andmay also be a text-supporting agent that responds in a text-based manneron the client terminal, such as a smartphone, or the like.

Also, the communication control system according to the presentembodiment may be installed in an information processing apparatus suchas a smartphone, a tablet terminal, or a PC, and may also be built intoa home system, an in-vehicle system, or a client-server system includinga client terminal and a server. In addition, the communication controlsystem according to the present embodiment may also be installed in ananthropomorphic device, such as a robot. In the case of a robot, inaddition to speech dialogue, expression control and action control mayalso be executed.

<<2. Configuration>> <2-1. System Configuration>

Next, an overall configuration of the communication control systemaccording to the present embodiment described above will be describedwith reference to FIG. 2. FIG. 2 is a diagram illustrating the overallconfiguration of the communication control system according to thepresent embodiment.

As illustrated in FIG. 2, the communication control system according tothe present embodiment includes a client terminal 1 and an agent server2.

The agent server 2 connects to the client terminal 1 through a network3, transmitting and receiving data. Specifically, the agent server 2generates response speech with respect to uttered speech collected andtransmitted by the client terminal 1, and transmits the response speechto the client terminal 1. The agent server 2 includes a phoneme database(DB) corresponding to one or more agents, and is capable of generatingresponse speech in the voice of a specific agent. Herein, the agents maybe characters from a comic book, anime, game, drama, movie, or the like,person such as celebrities, historical figures, or the like, but mayalso be average persons of different generations, without being specificindividuals, for example. Additionally, the agents may also be animalsor anthropomorphic characters. Additionally, the agents may also be aperson reflecting the personality of the user oneself, or personsreflecting the personality of the user's friends, family members,acquaintances, or the like.

Also, the agent server 2 is capable of generating response contentreflecting the personality of each agent. Through the agent, the agentserver 2 may a variety of services through dialogue with the user, suchas management of the user's schedule, the transmission and reception ofmessages, and information provision.

Note that the client terminal 1 is not limited to a smartphone asillustrated in FIG. 2, and may also be, for example, a mobile phoneterminal, a tablet terminal, a personal computer (PC), a game console, awearable terminal (such as smart eyeglasses, a smart band, a smartwatch, or a smart neckband), or the like. Additionally, the clientterminal 1 may also be a robot.

The above describes a summary of the communication control systemaccording to the present embodiment. Next, the configuration of theagent server 2 of the communication control system according to thepresent embodiment will be described specifically with reference to FIG.3.

<2-2. Agent Server 2>

FIG. 3 is a block diagram illustrating an example of the configurationof the agent server 2 according to the present embodiment. Asillustrated in FIG. 3, the agent server 2 includes a speech agentinterface (I/F) 20, a dialogue processing section 30, a phoneme storagesection 40, a conversation DB generation section 50, a phoneme DBgeneration section 60, an advertisement insertion processing section 70,an advertisement DB 72, and a feedback acquisition processing section80.

The speech agent I/F 20 functions as an input/output section of speechdata, a speech recognition section, and a speech generation section. Forthe input/output section, a communication section that transmits andreceives with the client terminal 1 through the network 3 isanticipated. The speech agent I/F 20 is capable of receiving the user'suttered speech from the client terminal 1, and converting the speech totext by speech recognition. In addition, the speech agent I/F 20converts response data (text) of the agent output from the dialogueprocessing section 30 into speech by using phoneme data corresponding tothe agent, and transmits the generated response speech of the agent tothe client terminal 1.

The dialogue processing section 30 functions as a computationalprocessing device and control device, and controls overall operationinside the agent server 2 by following various programs. The dialogueprocessing section 30 is realized by an electronic circuit such as acentral processing unit (CPU) or a microprocessor, for example. Inaddition, the dialogue processing section 30 according to the presentembodiment functions as a basic dialogue processing section 31, acharacter A dialogue processing section 32, a person B dialogueprocessing section 33, and a person C dialogue processing section 34.

The character A dialogue processing section 32, the person B dialogueprocessing section 33, and the person C dialogue processing section 34realize dialogue specialized for each agent. Herein, “character A”,“person B”, and “person C” are given as an example of the agents, butthe present embodiment obviously is not limited thereto, and may alsoinclude dialogue processing sections that realize dialogue specializedfor each of an even greater number of agents. The basic dialogueprocessing section 31 realizes general-purpose dialogue that is notspecialized for each agent.

Herein, a basic configuration common to the basic dialogue processingsection 31, the character A dialogue processing section 32, the person Bdialogue processing section 33, and the person C dialogue processingsection 34 will be described with reference to FIG. 4.

FIG. 4 is a diagram illustrating an exemplary configuration of adialogue processing section 300 according to the present embodiment. Asillustrated in FIG. 4, the dialogue processing section 300 includes aquestion search section 310, a response generation section 320, aphoneme data acquisition section 340, and a conversation DB 330. In theconversation DB 330, conversation data containing pairs of question dataand response data is saved. In a dialogue processing section specializedfor an agent, conversation data specialized for the agent is saved insuch a conversation DB 330, whereas in the general-purpose dialogueprocessing section, general-purpose conversation data (that is, basicconversation data) not specialized for an agent is saved in such aconversation DB 330.

The question search section 310 searches the conversation DB 330 forquestion data matching a question obtained by recognizing questionspeech (one example of uttered speech) of the user output from thespeech agent I/F 20 and converting the speech to text. The responsegeneration section 320 extracts, from the conversation DB 330, responsedata saved in association with the question data returned by the searchby the question search section 310, and generates response data. Thephoneme data acquisition section 340 acquires, from the phoneme storagesection 40 of the corresponding agent, phoneme data for converting theresponse generated by the response generation section 320 to speech. Forexample, in the case of the character A dialogue processing section 32,phoneme data for playing back the response data in the voice of thecharacter A is acquired from a character A phoneme DB 42. Subsequently,the dialogue processing section 300 outputs the generated response dataand the acquired phoneme data to the speech agent I/F 20.

The phoneme storage section 40 stores a phoneme database for generatingspeech for each agent. The phoneme storage section 40 may be realized byread-only memory (ROM) and random access memory (RAM). In the exampleillustrated in FIG. 3, a basic phoneme DB 41, a character A phoneme DB42, a person B phoneme DB 43, and a person C phoneme DB 44 are stored.In each phoneme DB, sub-phonetic segments and their control information,namely a prosody model, for example, are stored as phoneme data.

The conversation DB generation section 50 includes a function ofgenerating the conversation DB 330 of the dialogue processing section300. For example, the conversation DB generation section 50 collectsanticipated question data, and after collecting response datacorresponding to each question, saves pairs of question data andresponse data. Subsequently, when a predetermined amount of conversationdata (pairs of question data and response data, for example 100 pairs)is collected, the conversation DB generation section 50 registers theconversation data in the conversation DB 330 as a conversation data setof an agent.

The phoneme DB generation section 60 includes a function of generatingthe phoneme DB stored in the phoneme storage section 40. For example,the phoneme DB generation section 60 analyzes speech information fromreading predetermined text aloud, decomposes the speech information intosub-phonetic segments and their control information, namely a prosodymodel, and when a predetermined amount or greater of speech informationis collected, the phoneme DB generation section 60 executes a process ofregistering the speech information in the phoneme DB as phoneme data.

The advertisement insertion processing section 70 includes a function ofinserting advertisement information into the dialogue of the agent. Theadvertisement information to insert may be extracted from theadvertisement DB 72. In the advertisement DB 72, advertisementinformation (for example, advertisement content such as text, images,and speech, and information such as the advertiser, the advertisementperiod, and the advertising target) requested from the providing side(vendor, supplier), such as a corporation, is registered.

The feedback acquisition processing section 80 includes a function forinserting questions for acquiring feedback into the dialogue of theagent, and obtaining feedback from the user.

The above specifically describes a configuration of the agent server 2according to the present embodiment. Note that the configuration of theagent server 2 according to the present embodiment is not limited to theexample illustrated in FIG. 3. For example, each configuration includedin the agent server 2 may also be configured as another server on arespective network.

Next, specific operating processes of the communication control systemaccording to the present embodiment will be described with reference toFIGS. 5 to 14.

<<3. System Operating Processes>> <3-1. Conversation Data RegistrationProcess>

FIG. 5 is a flowchart illustrating a process of generating theconversation DB 330 according to the present embodiment. As illustratedin FIG. 5, first, the conversation DB generation section 50 saves ananticipated question (S103).

Next, the conversation DB generation section 50 saves a responsecorresponding to (paired with) the question (step S106).

Next, the conversation DB generation section 50 determines whether ornot a predetermined number of pairs of questions and responses (alsodesignated conversation data) have been collected (step S109).

Subsequently, in the case in which the predetermined number of pairs ofquestions and conversations have been collected (step S109/Yes), theconversation DB generation section 50 registers a data set includingmany pairs of questions and responses in the conversation DB 330 (stepS112). As an example of pairs of questions and responses, something likethe following is anticipated, for example.

Examples of pairs of questions and responses

Pair 1

-   -   Question: Good morning.    -   Response: How are you feeling today?

Pair 2

-   -   Question: How's the weather today?    -   Response: The weather today is OO.

Such pairs may be registered in the conversation DB 330 as conversationdata.

<3-2. Phoneme DB Generation Process>

FIG. 6 is a flowchart illustrating a process of generating the phonemeDB according to the present embodiment. As illustrated in FIG. 6, first,the phoneme DB generation section 60 displays an example sentence (stepS113). The display of the example sentence displays an example sentenceneeded for phoneme data generation on the display of an informationprocessing terminal not illustrated, for example.

Next, the phoneme DB generation section 60 records speech of the examplesentence being read aloud (step S116), and analyzes the recorded speech(step S119). For example, speech information of reading aloud by aperson in charge of the voice of the agent is collected by a microphoneof the information processing terminal, and the phoneme DB generationsection 60 receives and stores the speech information, and additionallyexecutes speech analysis.

Next, the phoneme DB generation section 60 generates a prosody model onthe basis of the speech information (step S122). A prosody model is anextraction of prosody parameters which indicate the prosodiccharacteristics (such as the pitch of sound, the loudness of sound, andthe speed of utterance, for example) of speech, and is different forevery person.

Next, the phoneme DB generation section 60 generates sub-phoneticsegments (phoneme data) on the basis of the speech information (stepS125).

After that, the phoneme DB generation section 60 saves the prosody modeland the sub-phonetic segments (step S128).

Next, the phoneme DB generation section 60 determines whether or not apredetermined number of prosody models and sub-phonetic segments havebeen collected (step S131).

Additionally, in the case in which the predetermined number of prosodymodels and sub-phonetic segments have been collected (step S131/Yes),the phoneme DB generation section 60 registers the prosody models andthe sub-phonetic segments in the phoneme storage section 40 as a phonemedatabase for a predetermined agent (step S134).

<3-3. Dialogue Control Process>

FIG. 7 is a flowchart illustrating a dialogue control process accordingto the present embodiment. As illustrated in FIG. 7, first, the speechagent I/F 20 checks whether or not question speech of the user and anagent ID have been acquired (step S143). The agent ID is identificationinformation indicating a specific agent, such as character A, person B,or person C. The user is able to purchase the phoneme data for eachagent, and the ID of the purchased agent is saved in the client terminal1 during the purchase process, for example.

Next, if question speech of the user and an agent ID is acquired (stepS146/Yes), the speech agent I/F 20 performs speech recognition and textconversion on the question speech (step S149). The speech agent I/F 20outputs the text-converted question to the dialogue processing sectionof the specific agent specified by the agent ID. For example, in thecase of “agent ID: character A”, the speech agent I/F 20 outputs thetext-converted question to the character A dialogue processing section32.

After that, the dialogue processing section 30 searches the conversationDB of the specific agent specified by the agent ID for a question thatmatches the text-converted question (step S152).

Next, in the case in which a matching question exists (step S155/Yes),the character A dialogue processing section 32 acquires response datacorresponding to (saved as a pair with) the question from theconversation DB of the specific agent (step S158).

On the other hand, in the case in which a matching question does notexist (step S155/No), the conversation DB of the basic dialogueprocessing section 31 is searched for a question that matches thetext-converted question (step S161).

In the case in which a matching question exists (step S161/Yes), thebasic dialogue processing section 31 acquires response datacorresponding to (saved as a pair with) the question from the basicdialogue processing section 31 (step S167).

On the other hand, in the case in which a matching question does notexist (step S164/No), the basic dialogue processing section 31 acquiresresponse data (for example, a response such as “I don't understand thequestion”) for the case of in which a matching question does not exist(step S170).

After that, the phoneme DB (herein, the character A phoneme DB 42) ofthe specific agent specified by the agent ID is referenced by thecharacter A dialogue processing section 32, and phoneme data of thecharacter A for generating speech of the response data is acquired (stepS173).

Next, the acquired phoneme data and the response data are output to thespeech agent I/F 20 (step S176)

Subsequently, the speech agent I/F 20 uses the phoneme data to convertthe response data (text) into speech (speech synthesis), and transmitsthe speech to the client terminal 1 (step S179). In the client terminal1, the response is played back in the voice of the character A.

<3-4. Conversation DB Update Process>

Next, a process of updating the conversation DB 330 of each dialogueprocessing section 300 will be described. In the present embodiment, itis possible to make the conversation DB 330 grow through conversationwith the user.

First, an exemplary data configuration of the conversation DB 330 willdescribed in further detail with reference to FIG. 8. FIG. 8 is adiagram explaining an exemplary data configuration of the conversationDB 330 according to the present embodiment. As illustrated in FIG. 8,each conversation DB 330 includes two layers, namely a personalizedlayer 331 and a common layer 332. For example, in the case of thecharacter A conversation DB 330A, conversation data reflecting thepersonality and characteristics of the character A is stored in thecommon layer 332A. Meanwhile, in the personalized layer 331A,conversation data that has been customized towards the user throughconversation with the user is stored. In other words, although thecharacter A phoneme DB 42 and the character A dialogue processingsection 32 are provided (sold) to users as a set, a certain user X and acertain user Y initially engage in dialogue with the same character A(the conversation data stored in the common layer 332A is used), but asthe users continue to engage in dialogue, conversation data customizedtowards each user is accumulated in the personalized layer 331A for eachuser. With this arrangement, it becomes possible to provide dialoguewith the character A that corresponds to what each of the user X and theuser Y likes.

Also, even in the case in which the agent “person B” is average personsof different generations without a specific personality like thecharacter A, conversation data may be customized towards the user. Inother words, in the case in which “person B” is “a person in his or her20s”, for example, average 20s conversation data is stored in the commonlayer 332B, and conversation data customized by continued dialogue withthe user is stored in the personalized layer 331B for each user.Additionally, the user is also able to select and purchase preferredphoneme data, such as “male”, “female”, “high-pitched voice”, or“low-pitched voice”, as the voice of the person B from the person Bphoneme DB 43.

A specific process when executing such customization of the conversationDB 330 will be described with reference to FIG. 9. FIG. 9 is a flowchartillustrating a process of updating the conversation DB 330 according tothe present embodiment.

As illustrated in FIG. 9, first, the speech agent I/F 20 acquires(receives) question speech of the user from the client terminal 1, andconverts the question speech to text by speech recognition (step S183).The text-converted data (question data) is output to the dialogueprocessing section (herein, the character A dialogue processing section32, for example) of the specific agent specified by the agent ID.

Next, the character A dialogue processing section 32 determines whetheror not the question data is a predetermined command (step S186).

After that, in the case of the predetermined command (step S186/Yes),the character A dialogue processing section 32 registers user-specifiedresponse data as a pair with the question data in the personalized layer331A of the conversation DB 330A (step S189). The predetermined commandmay be a word such as “NG” or “Settings”, for example. For example, by aflow of conversation like the following, the conversation DB of thecharacter A may be customized.

User: “Good morning”

Character A: “Good morning”

User: “NG. Say cheer up and do your best”

Character A: “Cheer up and do your best”

In the above flow of conversation, “NG” is the predetermined command,and after “NG” is uttered by the user, the character A dialogueprocessing section 32 registers the user-specified response data “Cheerup and do your best” as a pair with the question data “Good morning” inthe personalized layer 331A of the conversation DB 330A.

On the other hand, in the case of not the predetermined command (stepS186/No), the character A dialogue processing section 32 searches thecharacter A conversation DB 330A for response data stored as a pair withthe question data. In the case in which response data stored as a pairwith the question data is not stored in the character A conversation DB330A, that is, in the case in which the user's question is a questionwith no response (step S192/Yes), the character A dialogue processingsection 32 registers a user-specified response in the personalized layer331A as a pair with the question (step S195). For example, by a flow ofconversation like the following, the conversation DB of the character Amay be customized.

User: “How's it going?”

Character A: “I don't understand the question” (example response datafor the case in which a corresponding response does not exist)

User: “If I ask ‘How's it going?’, say ‘I'm great today as usual’”

Character A: “I'm great today as usual”

In the above flow of conversation, since there is no response datastored as a pair with “How's it going?”, example response data for thecase in which a corresponding response does not exist, namely “I don'tunderstand the question”, is acquired by the character A dialogueprocessing section 32, output together with the phoneme data of thecorresponding character A to the speech agent I/F 20, and played back bythe client terminal 1. After that, if the user-specified response “I'mgreat today as usual” is input, the character A dialogue processingsection 32 registers the response as a pair with the question data“How's it going?” in the personalized layer 331A.

Note that in the case of a question having a response (step S192/No),the character A dialogue processing section 32 acquires and outputs theresponse data together with the phoneme data of the correspondingcharacter A to the speech agent I/F 20, and the response is played backin the voice of the character A by the client terminal 1 (step S198).

Next, the movement of conversation data from the personalized layer tothe common layer will be described with reference to FIG. 10. FIG. 10 isa flowchart illustrating a process of moving conversation data from apersonalized layer to a common layer according to the presentembodiment. Herein, a process of moving conversation data from thepersonalized layer 331A to the common layer 332A of the character Adialogue processing section 32 will be described as an example.

As illustrated in FIG. 10, first, the character A dialogue processingsection 32 periodically searches the personalized layer 331A for eachuser (step S203), and extracts conversation pairs (pairs of questiondata and response data) with substantially the same content (step S206).As conversation pairs with substantially the same content, for example,the pair of the question “How's it going?” and the response “I'm greattoday as usual!”, and the pair of the question “How are you?” and theresponse “I'm great today as usual!”, differ only in the politeness ofthe question, and may be judged to be conversation pairs withsubstantially the same content.

Next, in the case in which a predetermined number or more ofconversation pairs have been extracted from the personalized layer 331Afor each user (step S209/Yes), the character A dialogue processingsection 32 registers the conversation pairs in the (in each user's)common layer 332A (step S212).

In this way, by moving conversation pairs having substantially the samecontent in the personalized layer 331 for each user to the common layer332, it becomes possible to make the common layer 332 grow (expand theconversation pairs).

Additionally, in the present embodiment, it is also possible to cause aconversation DB for basic dialogue to grow by moving conversation datafrom the conversation DB (specifically, the common layer) of a specificagent to the conversation DB for basic dialogue. FIG. 11 is a diagramexplaining the moving of conversation data to the basic dialogueconversation DB 330F according to the present embodiment. For example,in the case in which the user X and the user Y have each selected(purchased) the agent “character A”, while a user Z has selected(purchased) the agent “person B”, as illustrated in FIG. 11, aconversation DB 330A-X for the character A of user X, a conversation DB330A-Y for the character A of user Y, and a conversation DB 330B-Z forthe person B of user Z may exist in the dialogue processing section 30.In this case, in each personalized layer 331A-X, 331A-Y, and 331B-Z,individual (customized) conversation pairs are registered according tothe dialogue with each of the user X, the user Y, and the user Z (seeFIG. 9). Next, if there are a predetermined number of conversation pairswhich are substantially the same in the personalized layers 331A-X and331A-Y of the same agent, the conversation pairs are registered in eachof the common layers 332A-X and 332A-Y for each user (see FIG. 10).

Additionally, in the case in which a predetermined number or more ofconversation pairs which are substantially the same is extracted fromthe common layers 332A-X, 332A-Y, and 332B-Z of multiple agents (whichmay also include different agents), the dialogue processing section 30moves the conversation pairs to the higher-layer basic dialogueconversation DB 330F. The basic dialogue conversation DB 330F is aconversation DB included in the basic dialogue processing section 31.With this arrangement, it becomes possible to make the basic dialogueconversation DB 330F grow (expand the conversation pairs). Such a datamovement process will be described specifically with reference to FIG.12. FIG. 12 is a flowchart illustrating a process of moving conversationdata to the basic dialogue conversation DB 330F according to the presentembodiment.

As illustrated in FIG. 12, first, the dialogue processing section 30periodically searches the common layers 332 of multiple conversation DBs330 (step S223), and extracts conversation pairs which are substantiallythe same (step S226).

Next, in the case in which a predetermined number or more conversationpairs which are substantially the same have been extracted from themultiple common layers 332 (step S229/Yes), the dialogue processingsection 30 registers the conversation pairs in the basic dialogueconversation DB 330F (step S232).

In this way, by moving conversation pairs with substantially the samecontent in the common layer 332 of the conversation DB 330 for multipleagents to the basic dialogue conversation DB 330F, it becomes possibleto make the basic dialogue conversation DB 330F grow (expand theconversation pairs).

<3-5. Advertisement Insertion Process>

Next, the process of inserting advertisement information by theadvertisement insertion processing section 70 will be described withreference to FIGS. 13 and 14. In the present embodiment, by theadvertisement insertion processing section 70, it is possible to insertadvertisement information stored in the advertisement DB 72 into anutterance of an agent. Advertisement information may be registered inthe advertisement DB 72 in advance. FIG. 13 is a diagram illustrating anexample of advertisement information registered in the advertisement DB72 according to the present embodiment.

As illustrated in FIG. 13, advertisement information 621 includes anagent ID, a question, advertisement content, a condition, and aprobability. The agent ID specifies the agent to utter the advertisementcontent, the question specifies the question of the user that acts as atrigger for inserting the advertisement content, and the advertisementcontent is the advertisement sentence to insert into the dialogue of theagent. Also, the condition is a condition on inserting the advertisementcontent, and the probability indicates the probability of inserting theadvertisement content. For example, in the example illustrated on thefirst row of FIG. 13, in the case in which the word “chocolate” isincluded in a question from a user who is 30 years old or less in adialogue with the agent “character A”, advertisement content stating“The new chocolate on sale from BB Co. contains a lot of milk and isdelicious” is inserted into the response. Also, since the user mightfeel annoyed if the advertisement content is inserted every time thetriggering question is uttered, in the present embodiment, theprobability of inserting the advertisement may also be set. Such aprobability may be decided according to the advertisement fee. Forexample, as the advertisement fee becomes higher, a higher probabilityis set.

Such a process of inserting advertisement content will be describedspecifically with reference to FIG. 14. FIG. 14 is a flowchartillustrating the process of inserting advertisement content according tothe present embodiment.

As illustrated in FIG. 14, first, the advertisement insertion processingsection 70 monitors the dialogue (specifically, the dialogue process bythe dialogue processing section 30) between the user and the agent (stepS243).

Next, the advertisement insertion processing section 70 determineswhether or not a question with the same content as a question registeredin the advertisement DB 72 has appeared in the dialogue between the userand the agent (step S246).

After that, in the case in which a question with the same content hasappeared (step S246/Yes), the advertisement insertion processing section70 checks the condition and probability of advertisement insertionassociated with the corresponding question (step S249).

Subsequently, on the basis of the condition and the probability, theadvertisement insertion processing section 70 determines whether or notthe advertisement can be output (step S252).

Next, in the case in which the advertisement can be output (stepS252/Yes), the advertisement insertion processing section 70 temporarilystops the dialogue process by the dialogue processing section 30 (stepS255), and inserts the advertisement content into the dialogue (stepS258). Specifically, the advertisement content is inserted into aresponse of the agent with respect to the question of the user, forexample.

Additionally, dialogue (conversation data) including the advertisementcontent is output from the dialogue processing section 30 to the speechagent I/F 20, transmitted from the speech agent I/F 20 to the clientterminal 1, and played back in the voice of the agent (step S261).Specifically, advertisement content may be presented to the user as anutterance of the character A through a conversation like the following,for example.

User: “Good morning”

Character A: “Good morning! How are you feeling today?”

User: “I'm great. I want to eat something delicious”

Character A: “They say the barbecue at CC is delicious”

In the above conversation, first, with respect to the user question“Good morning”, the corresponding response found in the conversation DBof the character A, namely “Good morning! How are you feeling today? ”is output as speech. After that, since the user question “I'm great. Iwant to eat something delicious” includes the question “I want to eatsomething delicious” that acts as a trigger for advertisement insertion(refer to the second row of FIG. 13), the advertisement insertionprocessing section 70 executes the advertisement insertion process, anda response stating the advertisement content “They say the barbecue atCC is delicious” is output in the voice of the character A.

The above describes a conversation data registration process, a phonemeDB generation process, a dialogue control process, a conversation DBupdate process, and an advertisement insertion process as basicoperating processes of the communication control system according to thepresent embodiment.

Note that the dialogue control process according to the presentembodiment is not limited to the example described above. The dialogueprocessing section 30 according to the present embodiment is alsocapable of connecting dialogue with the agent seamlessly tocommunication with a person in the real world. Hereinafter, such a casewill be described specifically with reference to FIGS. 15 to 48.

<<4. Dialogue Control Process>> <4-1. Configuration> (4-4-1 SystemConfiguration)

FIG. 15 is a diagram illustrating an exemplary system configuration ofthe communication control system according to the present embodiment. Inthe present embodiment, as one example, a case of using the memberinformation of a marriage consulting service according to dialoguebetween a user and an agent to match the user with a marriage-seekingmember will be described.

As illustrated in FIG. 15, the communication control system according tothe present embodiment includes a client terminal 1, an agent server 2,and a management server 4. The management server 4 includes a functionof managing the member information of a marriage consulting service(marriage-seeking member information 41), and provides memberinformation in response to a request from the agent server 2.

(4-1-2. Configuration of Dialogue Processing Section 30 a)

Next, an exemplary configuration of the dialogue processing section 30 aincluded in the agent server 2 according to the present embodiment willbe described with reference to FIG. 16. The dialogue processing section30 a according to the present embodiment realizes a real-world workingagent system that connects dialogue with an agent seamlessly tocommunication with a person in the real world.

FIG. 16 is a diagram illustrating an exemplary configuration of thedialogue processing section 30 a according to the present embodiment. Asillustrated in FIG. 16, the dialogue processing section 30 a includes abasic dialogue processing section 31, a character A dialogue processingsection 32, a person B dialogue processing section 33, a person Cdialogue processing section 34, a matching section 35, and acommunication section 36.

The basic dialogue processing section 31, the character A dialogueprocessing section 32, the person B dialogue processing section 33, andthe person C dialogue processing section 34 are as described earlierwith reference to FIG. 3. The character A, the person B, and the personC are all examples of agent characters.

The communication section 36 may transmit and receive data with respectto an external apparatus over a network. For example, the communicationsection 36 receives marriage-seeking member information from themanagement server 4, and transmits an acceptance notification to amatching partner.

The matching section 35 includes a function of matching, according todialogue between a user and an agent, a real person having a personalityand preferences similar to the agent. A detailed configuration of thematching section 35 will be described next with reference to FIG. 17.

(4-1-3. Configuration of Matching Section 35)

FIG. 17 is a diagram illustrating an exemplary configuration of thematching section 35 according to the present embodiment. As illustratedin FIG. 17, the matching section 35 includes a user agent dialogueprocessing section 350, a user/agent information management section 351,a user information DB 352, an agent information DB 360, an agentlearning section 353, a user agent DB 354, a user-to-introduce selectionsection 355, a closeness computation section 356, an introductionprocessing section 357, a scenario management section 358, and ascenario DB 359.

The user/agent information management section 351 includes a function ofregistering, modifying, updating, deleting, and the like userinformation with respect to the user information DB 352 or agentinformation with respect to the agent information DB 360. The userinformation is input by the user on the client terminal 1, for example,and transmitted to the agent server 2. Alternatively, the userinformation is extracted from the marriage-seeking member information 41of the management server 4. The user information includes basicinformation, such as a user ID, age, occupation, family structure,living with family members, income, place of residence, and blood type.Each item includes a public attribute regarding whether or not to makethe item public to the final introduced partner (matching partner).Also, the user information includes basic condition information thatspecifies a preferred partner. The basic condition information indicatesthe user's desired conditions on information that may act as an itemwhen searching for a matching partner with the marriage consultingservice, such as age, occupation, family structure, living with familymembers, income, place of residence, and blood type. A priority levelmay be set for each item, and it may also be possible to emphasizehigh-priority items to initially select an agent having such attributes.Also, the user information includes user preference information. For thepreference information, for example, a numerical value may be set in arange from −1.0 (dislike) to 1.0 (like) for each item, or informationinput in a survey format, such as “strongly like, like, no opinion,dislike, strongly dislike”, may be registered. Also, the user preferenceinformation may be input by the user in advance, or preferenceinformation may be generated and edited according to dialogue betweenthe user and the agent. For example, in the case in which the user says“I have a passion for ramen” during a dialogue with the agent, a “ramen”item may be generated automatically, and “1.0” may be set.

Similar information regarding agents are stored in the agent informationDB 360. Default values are set in basic information, basic conditioninformation, and preference information about agents.

Note that specific examples of user and agent basic information and thelike are illustrated in FIGS. 24 to 27.

The agent learning section 353 includes a function of changing thepersonality (attributes) of the user agent in the user agent dialogueprocessing section 350 to the user's preferred personality by learningdialogue with the user. The initial value in the user agent dialogueprocessing section 350 is an agent selected by the user from amongmultiple agent characters prepared in advance, for example, and changesto the user's preferred agent by the agent learning section 353gradually through continued dialogue. In the user agent DB 354, theattributes (for example, each item in the basic information, basiccondition information, and preference information) of the user agentwhich have been changed to the user's preferred attributes by the agentlearning section 353 are stored, and updated as appropriate. Inaddition, the attributes of the user agent may also include the agent'sappearance (such as the type of face, hairstyle, and type of clothing).

The user agent dialogue processing section 350 includes a function ofrealizing automatic dialogue with the user through the user agent whichis appropriately changed by the agent learning section 353.Specifically, the user agent dialogue processing section 350 analyzesuttered speech or text transmitted from the client terminal 1, andoutputs a corresponding response.

The user-to-introduce selection section 355 includes a function ofsearching for a real person having attributes similar to the attributesof the agent changed to the user's preferences (the user agent), andmatching to a user as a user to introduce. The attributes of the agentchanged to the user's preferences are grasped by referencing the useragent DB 354. Specifically, the user-to-introduce selection section 355searches the marriage-seeking member information 41 for a real personhaving attributes similar to the agent changed to the user'spreferences, and extracts such a person as a user to introduce.Additionally, the attributes of the user to introduce may also be outputto the agent learning section 353 to make the user agent more similar tothe attributes of the user to introduce extracted by theuser-to-introduce selection section 355.

The closeness computation section 356 computes the closeness between theuser agent reflecting the attributes of the user to introduce, and theuser. For example, the closeness computation section 356 computes thecloseness according to the content of dialogue between the user agentand the user.

In the case in which the closeness computed by the closeness computationsection 356 exceeds a threshold value, the introduction processingsection 357 executes various processor for introducing theuser-to-introduce (a real person) to the user. For example, theintroduction processing section 357 transmits an approval notificationasking the user-to-introduce whether to agree to being introduced ornot, and displays an introduction screen on the client terminal 1 ofboth users.

The scenario management section 358 executes various processes thatsupport a meeting between the user and the user-to-introduce, inaccordance with a scenario registered in the scenario DB 359. Forexample, the scenario management section 358, following a scenarioselected arbitrarily by the user from the scenario DB 359, notifies bothuses of the time and place, and controls a notification of the plancontent to a predetermined business.

The above describes the configuration of the matching section 35according to the present embodiment.

(4-1-4. Configuration of Client Terminal 1)

Next, a configuration of the client terminal 1 according to the presentembodiment will be described with reference to FIG. 18. FIG. 18 is ablock diagram illustrating an example of a configuration of the clientterminal 1 according to the present embodiment.

As illustrated in FIG. 18, the client terminal 1 includes a controlsection 100, communication section 101, an operation input section 102,a sensor 103, a camera 104, a mic (an abbreviation of microphone) 105, adisplay section 106, a speaker 107, and a storage section 108.

(Control Section 100)

The control section 100 is realized by a processor such as a centralprocessing unit (CPU) included in the client terminal 1, for example.The control section 100 according to the present embodiment controls theplayback from the speaker 107 of response speech of the agenttransmitted from the agent server 2 through the communication section101, and controls the display of an image of the agent on the displaysection 106, for example.

Also, the control section 100 transmits selection information (such asthe selection of an agent, for example) by the user input from theoperation input section 102 to the agent server 2 through thecommunication section 101.

Also, the control section 100, under control by the agent server 2,controls the provision of an agent service such as automatic dialogue bythe agent selected by the user.

(Communication Section 101)

The communication section 101 is a communication interface including acommunication device and the like for connecting to the network 3, forexample. The communication section 101 may be a local area network(LAN), Bluetooth (registered trademark), Wi-fi, or Wireless USB (WUSB)communication card or the like, for example. Additionally, thecommunication section 101 may also be an optical communication router,an asymmetric digital subscriber line (ADSL) router, a modern for any ofvarious types of communication, or the like. The communication section101 transmits and receives signals or the like to and from the Internetor another communication device using a predetermined protocol such asTCP/IP, for example. Also, the network 3 connected to the communicationsection 101 is a network connected in a wired or wireless manner, andmay include the Internet, a home LAN, infrared communication, radio-wavecommunication, satellite communication, or the like, for example.

(Operation Input Section 102)

The operation input section 102 includes a function of receiving theinput of user operations, and outputting to the control section 100. Theoperation input section 102 is realized by a mouse, a keyboard, a touchpanel, buttons, switches, levers, or the like, for example.

(Sensor 103)

The sensor 103 includes a function of detecting the user or surroundingconditions. For example, the sensor 103 is realized by a biologicalsensor (such as a pulse monitor, a heart rate monitor, a perspirationsensor, a body temperature sensor, a blood pressure sensor, or anelectroencephalograph), an environment sensor (such as a temperaturesensor, an illumination sensor, or a pressure sensor), an accelerationsensor, a gyro sensor, a direction sensor, a vibration sensor, apositioning sensor, or the like.

(Camera 104)

The camera 104 includes each of a lens system including an imaging lens,a diaphragm, a zoom lens, a focus lens, and the like, a driving systemthat causes the lens system to execute focus operations and zoomoperations, a solid-state image sensor array that generates an imagingsignal by photoelectric conversion of imaging light obtained with thelens system, and the like. The solid-state image sensor array may berealized by a charge-coupled device (CCD) sensor array or acomplementary metal-oxide-semiconductor (CMOS) sensor array, forexample.

(Mic 105)

The mic 105 collects and outputs user speech and surrounding environmentsounds to the control section 100 as sound data.

(Display Section 106)

The display section 106 includes a function of displaying text,graphics, images, video, and the like. The display section 106 isrealized by a liquid crystal display (LCD) device, an organiclight-emitting diode (OLED) device, or the like, for example.

(Speaker 107)

The speaker 107 includes a function of playing back sound signals.

(Storage Section 108)

The storage section 108 stores programs and parameters by which thecontrol section 100 executes each function. For example, the storagesection 108 may store user information such as a user ID, name, age,gender, occupation, family structure, living with family members, age,place of residence, preference information, and the like.

Next, operating processes according to the present embodiment will bespecifically described with reference to FIGS. 19 to 27.

<4-2. Operating Processes>

Operating processes according to the present embodiment roughly includethe four phases of search, fixing, introduction, and support.

Search Phase

FIG. 19 is a flowchart illustrating an operating process in the searchphase according to the present embodiment. As illustrated in FIG. 19,first, when a single agent is selected by the user (step S270), the useragent dialogue processing section 350 starts dialogue with the user bythe selected agent (step S273).

Next, the agent learning section 353 learns the dialogue between theagent and the user by the user agent dialogue processing section 350,and changes the preferences and personality attributes of the agent tobe similar to the user's preferences (step S276).

Next, the user-to-introduce selection section 355 matches the agent madesimilar to the user's preferences to the attributes of each real person(partner available to be introduced), and selects the user to introduce(step S279). In this way, in the search phase, the agent attributes arechanged, and matching between the agent and a real person is executedbehind the scenes without the user's knowledge.

The above process is repeated until the number of times the same personis selected exceeds a predetermined number (step S282).

Subsequently, in the case in which the number of times the same personis selected exceeds the predetermined number (step S282/Yes), theprocess of the fixing phase described in FIG. 20 is started. Note thatalthough the number of times the same person is selected is treated asthe criterion herein, the present embodiment is not limited thereto, anda case in which the same person continues to be selected for a fixedperiod may also be treated as the criterion for starting the fixingphase.

Fixing Phase

FIG. 20 is a flowchart illustrating an operating process in the fixingphase according to the present embodiment. The fixing phase is a phasefor fixing the matching person (the selected person to introduce,hereinafter also designated the “real person”), and confirming whetherthe affinity (herein judged by “closeness”) between the person and theuser is truly satisfactory. Consequently, at this point, in the case inwhich the attributes of the real person change, the correspondingattributes of the agent are changed to match the real person.

Specifically, as illustrated in FIG. 20, first, the user agent dialogueprocessing section 350 continues dialogue between the user agent matchedto the real person, and the user (step S303).

Next, the agent learning section 353 continually checks the informationabout the real person saved in the user information DB 352, and in thecase in which the attributes of the matching real person change, theagent learning section 353 causes the changes to be reflected in theattributes of the user agent (step S306). Specifically, the attributesof the agent stored in the user agent DB 354 are updated to be moresimilar to the attributes of the real person.

Next, the closeness computation section 356 computes the closenessbetween the agent and the user (step S309). As described above, in thefixing phase, since the attributes of the agent are shifted towards thematching real person, there is a possibility of divergence in thepersonality and preferences of the user and the agent. Consequently, thecloseness between the agent resembling the real person and the user iscomputed, and it is determined whether or not the matching real personis truly suitable to the user. The computation of the closeness may becomputed on the basis of the degree of coincidence between agentattribute items and user attribute items, the amount of conversationwith the user, the amount of smiling by the user during conversation,and the like. Additionally, the closeness may also be computed from theuser's phrasing in the dialogue, the positivity/negativity of words, andthe like.

Next, in the case in which the closeness exceeds a predeterminedthreshold value (step S312/Yes), the introduction processing section 357notifies the matching real person of an introduction approval request(step S315).

Subsequently, in the case in which an introduction is approved by thereal person (step S318/Yes), the process of the introduction phasedescribed in FIG. 21 is started.

On the other hand, in the case in which an introduction is not approved(introduction is refused) by the real person (step S318/No), the flowreturns to the search phase illustrated in FIG. 19. Note that in thematching process of step S279 in the search phase, a person who hasalready refused an introduction approval is excluded.

Introduction Phase

FIG. 21 is a flowchart illustrating an operating process in theintroduction phase according to the present embodiment. In theintroduction phase, the user side is notified for the first time of theexistence of a real person who can be introduced.

As illustrated in FIG. 21, first, the introduction processing section357 notifies the user of the existence of a real person who can beintroduced to the user (step S323).

Next, the introduction processing section 357 displays a profile (to theextent that the other person has made public) of the real person tointroduce on the display section 106 of the client terminal 1 of theuser (step S326), and in addition, displays “want to meet/do not want tomeet buttons” for selecting whether the user wants or does not want toactually meet such a real person (hereinafter also designated the“introduced partner”) (step S329). With this arrangement, the user isable to check the profile of the introduced partner.

Next, dialogue is continued by speech or text between the user and thereal person through the agent (step S332). Herein, since theconversation is through the agent, utterances by the introduced partnerare played back in the voice of the agent, or chat is executed in astate in which a face image of the agent is displayed, for example. Theuser, having continued dialogue with the introduced partner through theagent, determines whether one wants or does not want to actually meetthe introduced partner, and taps the “want to meet/do not want to meetbuttons”. Such buttons may also be displayed on a screen on the otherperson' side.

Next, in the case in which both the user and the introduced partnerexpress (select the “want to meet button”) an intention to meet eachother (step S335/Yes), the support phase illustrated in FIG. 22 isstarted.

On the other hand, in the case in which either person expresses (selectsthe “do not want to meet button”) no intention to meet (step S338/Yes),the matching with the real person is canceled, and the flow returns tothe search phase illustrated in FIG. 19. At this time, the user is ableto select whether or not to select a different agent (step S341). In thepresent embodiment, since multiple agents are available, and since eachagent has a different personality, that is, attributes with differentinitial values, in the case of wanting to start over with an agent of adifferent type, the user selects a different agent. On the other hand,in the case of not wanting to select a different agent, the attributesof the current agent are changed to the user's preferred attributesagain according to dialogue with the user. Also, in the search phase, aperson whose matching has been canceled once is not matched for a fixedperiod.

Support Phase

FIG. 22 is a flowchart illustrating an operating process in the supportphase according to the present embodiment. Since a connection with areal person is achieved in the introduction phase, it is not strictlynecessary to execute the support phase, but the support phase may beperformed according to a user request. Herein, it is possible to selecta situation or arrangement, called a “scenario”, for when the two peoplemeet.

As illustrated in FIG. 22, first, a meeting plan is selected by the userfrom among multiple meeting plans (scenarios) stored in the scenario DB359 (step S353). Note that the scenario management section 358 may alsorecommend a meeting plan automatically from the content of dialoguebetween the user and the introduced partner so far. The meeting planincludes a place (such as a restaurant, park, amusement park, oraquarium), content (a surprise arrangement, a present, specialtreatment, a discount), a time (weekday/weekend, morning/noon/night),expenses, and the like. Specifically, for example, content such as “asurprise present for her at a quiet restaurant . . . ” is set in themeeting plan. Depending on the content, the meeting plan can only beviewed on the user side.

Next, the scenario management section 358, following the implementedmeeting plan, notifies the user and the real person of the time andplace of the meeting (step S356).

Next, the scenario management section 358 confirms whether or not theschedules agree with each other and are OK (step S359). Note that whenselecting the meeting plan, the user may be made to specify a time whenone's schedule is free.

Next, in the case in which both are OK (step S359/Yes), an arrangementbusiness that will execute the meeting plan is notified (step S362). Thearrangement business may be a facility such as a restaurant or anamusement park, for example. Since two people will be making use of thefacility according to the meeting plan, this has the merit of acting asan advertisement for the facility side.

Additionally, when it is confirmed that the two people have met on theappointed day (step S365/Yes), the arrangement by the business isexecuted (step S368). For example, a special menu may be prepared forthe introduced partner, the seats offering the best view may be madeavailable at a place with favorable scenery, or the like. Also,depending on the content of the arrangement, performers may be placednearby and talk about a tourist destination that could lead to the nextdate or talk about how wonderful marriage is, so that the users are ableto hear.

<4-3. Example>

Next, the present embodiment will be described in detail using anexample. Herein, an example of cooperating with a marriage consultingservice (management server 4) will be described. In this example, theuser who is using the system is hypothetically assumed to be an adultman, and is matched to a woman as the introduced partner on the basis ofmember information of the marriage consulting service. Also, in thepresent embodiment, a preparation phase for making preparations beforethe four phases described above will be described.

(4-3-1. Preparation Phase)

The user inputs a profile (user information) in preparation for usingthe system. Hereinafter, such a case will be specifically described withreference to FIG. 23.

As illustrated in FIG. 23, first, the user inputs user basic informationfrom the client terminal 1 (step S393). The information input at theclient terminal 1 is transmitted to the agent server 2, and stored inthe user information DB 352 by the user/agent information managementsection 351. FIG. 24A illustrates an example of user basic information.As illustrated in FIG. 24A, for example, the user basic informationincludes information about age, occupation, family structure, livingwith family members, income, place of residence, and blood type. Also,each item of the user basic information has a public attribute regardingwhether or not the item may be viewed by other people.

Next, the user inputs user basic condition information from the clientterminal 1 (step S396). The user basic condition information isinformation related to the attributes of an ideal introduced partner.FIG. 24B illustrates an example of user basic condition information. Asillustrated in FIG. 24B, for example, the user basic conditioninformation includes information about age, occupation, familystructure, living with family members, income, place of residence, andblood type. Also, the user is able to set a priority level for eachitem, making it possible to emphasize high-priority items to select theinitial agent. Note that the items of the user basic conditioninformation are not limited to the example illustrated in FIG. 24B, andit is also possible to include items such as appearance, personality,way of speaking, and tone of voice, for example.

Next, the user inputs user preference information from the clientterminal 1 (step S399). The user preference information is informationrelated to the preferences of the user. FIG. 25 illustrates an exampleof user preference information. In the user preference information, eachitem is set in a range from −1.0 (dislike) to 1.0 (like), for example.Also, the configuration is not limited to numerical input, and wheninputting the user preference information, it may also be possible toinput information in a survey format for each item, such as “stronglylike, like, no opinion, dislike, strongly dislike”.

Next, the user/agent information management section 351 executes anassessment of the user's personality (step S402). The user personalityassessment includes, for example, a method that classifies personalityinto several patterns on the basis of answers to set questions presentedto the user in a survey format, or computes a per-axis score on multipleaxes that act as elements of personality in a radar chart or a linegraph. In the case of the personality assessment known as an egogramtest, by asking the user questions such as “Do you have difficultysaying no?” and “Are you strict about being on time?”, for example,points may be aggregated ultimately for each of five axis elements(designated Controlling Parent (CP), Nurturing Parent (NP), Adult egostate (A), Free Child (FC), and Adapted Child (AC)), and a personalityassessment result can be expressed ultimately with a line graph calledan egogram. Although expressed using a line graph or a radar chart whenpresented to the user, by saving the points for each element as userpersonality information inside the user information DB 352, and bycomparing the points of elements for each user, the similarity betweenpersonalities and whether or not a personality is preferred by the usermay be determined.

Next, the user/agent information management section 351 executes apersonality assessment of the user's ideal person (step S405). Thepersonality of the user's ideal person can be set by, for example, theuser pretending to be one's ideal partner to input information into theegogram text. Similarly, in the personality assessment result of theuser's ideal person, points for each element are stored in the userinformation DB 352 as ideal personality information.

The basic information, basic condition information, preferenceinformation, personality information, and ideal personality informationdescribed above is also included similarly for agents, and is stored inthe agent information DB 360. An example of agent basic information isillustrated in FIG. 26A, an example of agent basic condition informationis illustrated in FIG. 26B, and an example of agent preferenceinformation is illustrated in FIG. 27. The basic information, basiccondition information, preference information, and ideal personalityinformation may be set as initial values for defining the personality ofthe agent.

The above describes the preparation phase specifically. Similar valuesto the basic information, basic condition information, preferenceinformation, personality information, and ideal personality informationprepared in the preparation phase are also stored in themarriage-seeking member information 41 for real persons(marriage-seeking members) on the marriage consulting service sidemanaged by the management server 4.

(4-3-2. Search Phase)

Next, an example of the search phase will be described specifically withreference to FIGS. 28 to 38.

When the preparation phase of inputting the basic information and thelike ends, the user is guided next to a screen for selecting an agent.FIG. 28 is a diagram illustrating an example of an agent selectionscreen according to the example. On the illustrated screen 200, an imageof an agent (such as a picture drawn by hand, CG, or a photograph), abasic profile of the agent, and the agent's personality and preferencesare displayed. The agent's personality may be displayed with apersonality chart as illustrated in FIG. 28, or with a line graph by theegogram described above. Also, agent selection candidates may begenerated to reflect the user's preferences. Also, the agent selectioncandidates may be chosen by matching to the user's preferences fromamong agents with profiles set in the agent information DB 360, orchosen irrespectively of the user's preferences on the basis of anindex, such as order of popularity or order of rating, for example.

Hereinafter, a specific operating process will be described withreference to FIGS. 29A and 29B.

FIGS. 29A and 29B are flowcharts illustrating an operating process in asearch phase according to the example. As illustrated in FIG. 29A,first, an agent is selected by the user (step S413 to step S437). Threeagent selection methods are available, for example. The user is able toselect an agent by a preferred selection method.

In the first selection method (step S413/Yes), images of multiple femaleagents registered in the agent information DB 360 are displayed, and theuser selects a female agent of preferred appearance (step S416).

Next, the agent learning section 353 extracts the user's ideal basicconditions and ideal personality input in the preparation phase from theuser information DB 352, and sets (registers in the user agent DB 354)the information directly in the selected female agent (step S419, stepS422).

In the second selection method (step S425/Yes), on the basis of the userbasic condition information and ideal personality information input inthe preparation phase, the agent learning section 353 searches the agentinformation DB 360 for a female agent having close attributes (stepS428, step S431), presents search results to the user, and receives anagent selection (step S434). During the agent search, the userpreference information additionally may be taken into account to searchfor a female agent having preference information resembling the user's.Also, among each of the items of the basic condition information,high-priority items may be prioritized to narrow down the candidates tofemale agents with the most matching attributes.

In the third selection method (step S425/No), the user selectsarbitrarily from among all agents while checking the appearance and theprofile of the agents (step S437).

Next, as illustrated in FIG. 29B, the user agent dialogue processingsection 350 acquires an activity history and biological informationabout the user (step S440, step S443). A user activity history refers toa history of places where the user has been active, an activity contenthistory, a purchase history, an SNS posting history, and the like, andis acquired from the client terminal 1 or a predetermined server. Useractivity may be grasped in detail from position information, an Internethistory, activity recognition based on acceleration sensor data and gyrosensor data, and the like. Also, user biological information is acquiredin real time from the client terminal 1, and the current state of theuser (such as being nervous, sleepy, or smiling) is grasped.

Next, the user agent dialogue processing section 350 executes thedialogue process with the user by the agent (step S446). At this time,the user agent dialogue processing section 350 may generate responsesreferencing the acquired user activity history and biologicalinformation, and output the responses as utterances of the agent. FIGS.30 to 32 illustrate an example of dialogue by the agent utilizing theuser activity history and biological information. Herein, dialogue isexecuted between the agent and the user through a chat screen on thedisplay section 106.

FIG. 30 is a diagram explaining exemplary utterances of the agentutilizing the user activity history according to the example. On theillustrated screen 202, a dialogue between a user M and an agent “Saki”is displayed in a chat format. On the bottom of the screen 202, a textinput field and a Send button are displayed, such that by the userinputting a message into the text input field and tapping the Sendbutton, the input message is transmitted to the agent server 2 asutterance text.

The user agent dialogue processing section 350 generates responses ofthe agent “Saki” on the basis of a data set of preset questions andresponses, for example. Specifically, with respect to a question such as“I was hooked on OO”, “I'm obsessed with OO”, or “I have a passion forOO”, for example, the response “Do you like OO?” is given. Also, it isalso possible for the user agent dialogue processing section 350 toreference an activity history, such as the user M getting off the trainat Y Station today, for example, and output an utterance stating “Thereare a lot of ramen shops around Y Station where you got off the traintoday!”, as illustrated in FIG. 30. With this arrangement, the user Mmay feel a sense of familiarity with the agent, and a growing perceptionof the agent as one's very own partner may be anticipated.

FIG. 31 is a diagram explaining exemplary utterances of the agentutilizing biological information about the user according to theexample. Biological information is obtained from a biological sensorprovided in a wearable device worn by the user or a camera that capturesa face image of the user, and is transmitted from the client terminal 1to the agent server 2 in real time. The user agent dialogue processingsection 350 infers the state (psychological state, emotions) of the useron the basis of the biological information, and generates utterancesreflecting the inferred result as statements of the agent. For example,in the case in which the heart rate of the user M is faster than normal,as displayed on the screen 203 of FIG. 31, an utterance in tune with theother person's psychological state, such as “Huh? You seem different . .. ” or “I'm starting to feel kind of nervous myself”, is output. Withthis arrangement, it is anticipated that the psychological distance tothe agent will shorten, and the user will feel more intimate.

FIG. 32 is a diagram explaining exemplary utterances of an agentutilizing SNS information according to the example. The user agentdialogue processing section 350 searches SNS and websites for topics inagreement with the user preference information to use in utterances ofthe agent. For example, as displayed on the screen 204 of FIG. 32, anutterance such as “I don't know much about it, but it seems likeeverybody's talking about the draft for the AA team on SNS” is output.By providing topics matching the preferences of the user M in this way,it is anticipated that talk will become more lively, and the user M willfeel more enjoyment out of conversation with the agent.

The details of the dialogue process by the user agent dialogueprocessing section 350 described above are illustrated in FIGS. 33A and33B.

FIGS. 33A and 33B are flowcharts of the agent dialogue process accordingto the example. As illustrated in FIG. 33A, first, in the case in whicha fixed time has elapsed since the last conversation with the user (stepS473/Yes), the user agent dialogue processing section 350 issues agreeting (step S476).

Next, in the case in which a predetermined period continues in a statein which a user to introduce is not selected (step S479/Yes), the useragent dialogue processing section 350 generates and utters a personalityimprovement (or ideal improvement) message to the user (step S482). Theselection of a user to introduce will described in detail later.

Next, in the case in which there is new input (an utterance) from theuser (step S485/Yes), the agent server 2 executes speech recognition onthe uttered speech (step S488), and executes text analysis on theutterance text (step S491).

Next, the user agent dialogue processing section 350 of the agent server2 generates utterance candidates according to the attributes (each itemof the basic information, basic condition information, and preferenceinformation) of the user (step S494).

Next, in the case in which the user activity history is available (stepS497/Yes), the user agent dialogue processing section 350 refines theutterance candidates according to the activity history (step S498)

Also, in the case in which the user biological information is available(step S500/Yes), the user agent dialogue processing section 350 refinesthe utterance candidates according to the biological information (stepS503). Note that the user agent dialogue processing section 350 may alsorefine the utterance candidates using both the activity history and thebiological information.

Next, in the case in which an utterance candidate exists (stepS506/Yes), the user agent dialogue processing section 350 outputs theutterance candidate as a statement of the agent (step S509). Note thatin the case in which an utterance candidate does not exist (stepS506/No), the agent does not make a statement.

The agent dialogue described above is described as being a response inthe case in which there is new input (an utterance) from the user, butthe dialogue according to the example is not limited thereto, andstatements may also be made from the agent side.

Specifically, in the above step S485, in the case in which there is nonew input (an utterance) from the user (step S485/Yes), as illustratedin FIG. 33B, the user agent dialogue processing section 350 determineswhether or not a topic exists in the user activity history (step S512).For example, the user agent dialogue processing section 350 determines,from the activity history, whether or not noteworthy activity exists,such as going out to a tourist destination, getting off the train at astation different from usual, or making an expensive purchase.

Next, in the case in which a topic exists (step S512/Yes), the useragent dialogue processing section 350 generates utterance candidatesusing the activity history (step S515).

Next, it is determined whether or not a topic exists in the userbiological information (step S518) For example, the user agent dialogueprocessing section 350 determines, on the basis of the biologicalinformation, a noteworthy state or emotion that is different fromnormal, such as smiling, cheerfulness, sleepiness, tiredness,excitement, or anger.

Next, in the case in which a topic exists (step S518/Yes), the useragent dialogue processing section 350 generates utterance candidatesusing the biological information (step S521).

Next, recent SNS, websites, and the like are searched for topicspreferred by the user, and in the case in which a user-preferred topicexists (step S524/Yes), the user agent dialogue processing section 350generates an utterance candidates using the topic on the SNS or the like(step S527).

Note that although FIG. 33B illustrates the flow of generating utterancecandidates by utilizing any one of the activity history, biologicalinformation, SNS information, and the like, the example is not limitedthereto, and it is also possible to generate utterance candidates bycombining the information of any one or more among the activity history,biological information, SNS information, and the like.

Next, returning to FIG. 29B, the agent learning section 353 executesagent learning as appropriate, in accordance with the dialogue betweenthe agent and the user (step S449). Specifically, the agent learningsection 353 updates the attributes of the agent to the user's preferredattributes. Hereinafter, such a case will be described specifically withreference to FIGS. 34 and 35.

FIG. 34 is a flowchart illustrating an operating process of agentlearning according to the example. As illustrated in FIG. 34, first, theagent learning section 353 executes text analysis on the input(utterance text) of the user (step S530).

Next, the agent learning section 353 determines the userpositivity/negativity with respect to an object word (a phenomenon whichis a target of preference) (step S533).

Next, the agent learning section 353 checks whether or not the aboveobject word is an item included in the agent preference information(step S536), and if not included, adds an item of the object word to theagent preference information (step S539).

Next, the agent learning section 353 adjusts a preference level (anumerical value of preference) of the items corresponding to the agentpreference information (step S541). In other words, on the basis of thedialogue content, the agent learning section 353 discriminates the userpositivity/negativity with respect to a certain phenomenon, and causesthe discrimination result to be reflected in the agent preferenceinformation, and thereby is able to bring the agent's preferences closerto the user's preferences. Note that not only the agent preferenceinformation but also the agent basic information and basic conditioninformation may be made to reflect the user's preferences.

At this time, since it would be unnatural for the agent's preferencesand personality to change suddenly, the agent learning section 353 mayalso keep the updating of numerical values to an adjustment inside afixed range. For example, as illustrated in FIG. 35, in the case inwhich the user inputs “I was hooked on the ramen ranking on TV today,and now I want to eat some tonkotsu ramen. u think I'll get fat? Iwasn't interested in the light shoyu ramen.”, by text analysis, “TV”,“ramen ranking”, “tonkotsu ramen”, and “shoyu ramen” are extracted asobject words (preference target words). The agent learning section 353determines the positivity/negativity with respect to each object wordaccording to each corresponding phrase indicating the user's impressionwith respect to the object word, as illustrated in the table in theupper-right of FIG. 35. In the example illustrated in FIG. 35, since theuser is confirmed to have positive feelings about “ramen”, “ramen” isadded to the agent preference information, and the preference level (thenumerical value of preference) is raised to be more similar to theuser's preferences. At this time, the points to add may be variedaccording to words (such as “very much” or “a little”) expressing thestrength of preference, and for a single statement, for example, thevalue may be changed by 0.1 at a time. Also, the agent learning section353 may normalize the preference level such that 1.0 is the maximum and−1.0 is the minimum.

Next, the agent learning section 353 determines a category of the user'sphrasing (step S544). Herein, by changing the agent's phrasing to matchthe user's phrasing, the agent can be made to match the tone of theuser. Phrasing categories are divided into, for example, polite, blunt(gruff), kind, rowdy, fast, slow, and the like.

Next, the agent learning section 353 adjusts phrasing categoryparameters of the agent (step S547). The phrasing category parameters ofthe agent may be stored in the user agent DB 354. The phrasing categorydetermination may be made by treating the most frequent category amongthe phrasing categories collected from user dialogue in a fixed periodas a phrasing category representative of the user. For example, in theexample illustrated in FIG. 35, as illustrated in the lower-right ofFIG. 35, since the “polite” category is extracted three times while the“blunt” category is extracted one time, the phrasing categoryrepresentative of the user may be determined to be “polite”.Alternatively, the agent learning section 353 may treat the probabilityof occurrence of each phrasing category extracted from user dialogue asthe determination result. Subsequently, the agent learning section 353causes the phrasing category representative of the user or theprobability of occurrence of each phrasing category to be reflected inthe phrasing category parameters of the agent.

Next, the agent learning section 353 checks whether or not a change inthe preference information and phrasing parameters described aboveinfluences the agent's personality (step S550), and if so, adjusts theparameters of the agent's personality information (step S553). Sincepersonality is expressed by one's preferences and phrasing, thepersonality parameters are adjusted in accordance with the changedpreferences and phrasing.

As described above, the agent gradually develops into the user'spreferred agent through continued dialogue between the agent and theuser.

Next, returning to FIG. 29B, the user-to-introduce selection section 355determines whether or not a matching update interval described next hasexceeded a threshold value (step S452). The matching update interval isset appropriately, such as to one day or one week, for example.

Next, the user-to-introduce selection section 355 executes matchingbetween a real person and the agent (step S455). In the example,matching between real persons and the agent is executed periodicallybehind the scenes as dialogue between the agent and the user progresses.Specifically, for example, the user-to-introduce selection section 355references the marriage-seeking member information 41 to search for areal person resembling the agent. As described above, since the useragent is changing into the user's preferred agent, by matching a realperson resembling the agent, a partner who is compatible with the usercan be found more effectively. Hereinafter, such a case will bedescribed specifically with reference to FIGS. 36 and 38.

FIG. 36 is a flowchart illustrating an operating process of matchingaccording to the example. As illustrated in FIG. 36, first, theuser-to-introduce selection section 355 searches the marriage-seekingmember information for persons who match all of the agent basiccondition information (step S563). FIG. 37 is a diagram explaining asearch using the agent basic condition information. As illustrated inFIG. 37, the marriage-seeking member information is screened on thebasis of whether or not basic condition items satisfy the conditions.For example, the user-to-introduce selection section 355 sends the basiccondition information to the management server 4, and acquires one ormore member IDs satisfying the conditions from the marriage-seekingmember information 41.

Next, it is determined whether or not the number of found candidates(the number of marriage-seeking members) exceeds a threshold value(fixed number) (step S566). With this arrangement, a number ofcandidates from which to choose the matching partner, such as a minimumof 10 people, for example, can be secured.

Next, in the case in which the number of candidates does not exceed thethreshold value (step S566/No), the user-to-introduce selection section355 searches for candidates who satisfy items of “high/medium” priorityin the basic condition information (step S569).

Next, in the case in which the number of candidates still does notexceed the threshold value (step S572/No), the user-to-introduceselection section 355 additionally searches for candidates who satisfyitems of “high” priority in the basic condition information (step S575).

Next, in the case in which the number of candidates exceeds thethreshold value (step S578/Yes), the user-to-introduce selection section355 computes the correlation between the agent and the personalityassessment result of each candidate (step S581). At this point, assumethat each marriage-seeking member has also conducted the personalityassessment described in the preparation phase in advance. For example,the user-to-introduce selection section 355 adds up the square values ofthe difference for each value of the egogram between the agent and eachcandidate, and computes the cross-correlation.

Next, the user-to-introduce selection section 355 excludes candidateswhose correlation value is lower than a predetermined threshold value(step S584).

Next, the user-to-introduce selection section 355 computes thesimilarity of preferences between the remaining candidates and the agent(step S587), and selects the candidate (person P) of highest similarity(step S590). The computation of preference similarity is executed usingthe preference information.

Specifically, the user-to-introduce selection section 355 generates an-dimensional vector in which the numerical values of matching itemsamong each item of the preference information are arranged for the agentand a certain candidate, for example, and computes the cos θ innerproduct of the n-dimensional vector of the agent and the n-dimensionalvector of the candidate person. If the numerical value is 1.0, thesimilarity is a complete match, whereas if 0.0, the similarity is acomplete mismatch.

However, in the case in which n is smaller than a predeterminedthreshold value, the candidate person is excluded from the candidateswithout executing the similarity computation. This is because even if afew items match, the reliability is low.

For example, in the example illustrated in FIG. 38, among the preferenceinformation of the agent and the preference information of a candidateperson, namely a marriage-seeking member, the five items “AA Team”, “FFDining”, “D Town”, “Sushi”, and “EE Fashion” match. Consequently, theuser-to-introduce selection section 355 respectively generates5-dimensional vectors in which the numerical values of these five itemsare arranged.

On the other hand, in the case in which the number of candidates doesnot exceed the threshold value (step S578/No), since there are fewcandidates, matching is canceled, and the flow returns to step S440illustrated in FIG. 29B.

The above describes matching between the agent and a real person(selection of the user to introduce (person P)) according to the presentembodiment.

Next, returning to FIG. 29B, the user-to-introduce selection section 355checks whether or not the selected person P is the same as the personselected the last time (step S461). As described above, the matching ofa real person may be executed periodically (such as once per day, forexample) while dialogue between the agent and the user is executed.

Next, in the case in which the selected person P is the same as theperson selected the last time (step S461/Yes), the user-to-introduceselection section 355 increments a same person count Cs (step S464).

On the other hand, in the case in which the selected person P is not thesame as the person selected the last time (step S461/No), theuser-to-introduce selection section 355 resets the same person count Cs(step S470).

Next, the user-to-introduce selection section 355 periodically executesmatching with a real person until the same person count Cs exceeds athreshold value (step S467). In the case in which the same person countCs exceeds the threshold value (step S467/Yes), the fixing phaseillustrated in FIG. 39 is started.

(4-3-3. Fixing Phase)

Next, an example of the fixing phase will be described specifically withreference to FIGS. 39 to 41. In the fixing phase, while the user and theagent continue dialogue similarly as during the search phase, thepreferences and personality attributes of the agent are gradually mademore similar to the real person matched in the search phase, and theaffinity (closeness) with the real person is determined without the usernoticing.

FIG. 39 is a diagram illustrating an operating process in the fixingphase according to the example. As illustrated in FIG. 39, first, anawaiting approval flag is set to false (step S603).

Next, the closeness computation section 356 sets the closeness to 0(step S606).

Next, the dialogue process with the user utilizing the user activityhistory and biological information continues to be executed by the useragent dialogue processing section 350, similarly to the above steps S440to S446 (step S609 to step S615).

Next, the agent learning section 353 checks whether or not a change hasoccurred in the preferences, basic information, or personalityinformation attributes of the selected person P (step S618), and if achange has occurred, the agent learning section 353 causes the change tobe reflected in the attributes of the agent preference information,basic information, personality information, and the like (step S621). Atthis time, the changed value may be copied directly, but since a suddenchange in the agent's preferences may feel unnatural to the user in somecases, the amount by which to change a value at once may also belimited. The preferences of the agent are made to match the preferencesof the person P over a fixed time.

Herein, FIG. 40 illustrates a dialogue example in a case in which theagent's preferences have changed. Suppose that the agent “Saki”illustrated in FIG. 40 had an attribute of not particularly liking theAA team at the time of the search phase when the agent had been indialogue with the user M (in the search phase, since the agent isaligned gradually with the preferences of the user, the agent would havebecome a fan of the AA team over time). However, in the case in which areal person matched by other preferences and personality is selected,and the flow proceeds to the fixing phase, the preferences of the agent“Saki” now become similar to the preferences of the real person.Subsequently, in the case in which the real person coincidentally isalso a fan of the AA team, the preferences of the agent “Saki” areadjusted to like the AA team, and as illustrated on the screen 206 ofFIG. 40, for example, a more positive statement about the topic of theAA team, such as “I'm starting to like the AA team, too. Yesterday'sgame was great!”, is output.

Next, in the case in which the awaiting approval flag has not becometrue (step S624/Yes), the closeness computation section 356 computes thecloseness between the user and the agent (step S636). The method ofcomputing the closeness may be similar to the method of matching theagent to a real person executed by the user-to-introduce selectionsection 355 using the basic condition information, preferenceinformation, and the like. In other words, the degree of coincidence ofitems in the basic condition information, preference information, andthe like is computed as the closeness. Also, numerical values based onthe amount of conversation between the user and the agent and the amountof smiling during conversation additionally may be added to thecloseness.

Next, in the case in which the closeness exceeds a threshold value (stepS639/Yes), the introduction processing section 357 notifies the selectedperson P (the marriage-seeking member matched to the agent) of anintroduction approval request (step S642), sets the awaiting approvalflag to true, and waits for approval (step S645). Herein, FIG. 41illustrates an example of a notification screen of the introductionapproval request. In the illustrated example, on a screen 207, a profileof the user M (to the extent that the user M has made public) isdisplayed, and in addition, an Approve button 207 a and a Reject button207 b are displayed. The person P receiving the introduction approvalrequest notification checks the profile of the user M, and taps theApprove button 207 a in the case of agreeing to be introduced to theother person, or taps the Reject button 207 b in the case of notagreeing.

Next, the user M side is in the awaiting approval state until there isan approval response (step S648), during which dialogue with the agentand agent learning are continued (steps S609 to S624).

Next, in the case of a timeout with no response from the person P in afixed period (step S627/Yes), the introduction processing section 357sets the awaiting approval flag to false (step S630), and in addition,the closeness computation section 356 sets the closeness to 0 (stepS633). In this case, the matching with the person P is canceled, and theflow returns to the search phase.

Also, in the case in which the person P does not approve (step S651/No),the closeness is reset to 0 (step S633), the matching with the person Pis canceled, and the flow returns to the search phase.

On the other hand, in the case in which the person P approves (stepS651/Yes), the introduction phase illustrated in FIG. 42 is started.

(4-3-4. Introduction Phase)

Next, the introduction phase of the example will be described withreference to FIGS. 42 to 44. In the introduction phase, the approval bythe real person acts as a trigger, and the user side is notified of theexistence of a person who can be introduced. The user knows of theexistence of the real person from this point in time, and becomes ableto see the profile. Note that the profile of the real person presentedto the user side contains only the items with a public attribute of“Yes” in the basic information.

FIG. 42 is a flowchart illustrating an operating process in theintroduction phase according to the example. As illustrated in FIG. 41,first, the user-to-introduce selection section 355 notifies the user ofthe existence of a person who can be introduced (step S663). FIG. 43illustrates an example of the introduction notification. In theillustrated example, on a screen 208, a chat between the agent “Saki”and the user M is displayed. In the case in which the person Passociated with the agent “Saki” has approved an introduction, theuser-to-introduce selection section 355 interrupts with a messagestating “Sorry to interrupt, but the management has an announcement foryou! We have found a woman similar to Saki. Why don't you contact herfrom the heart button on the bottom of the screen?”. Since the messageis an interrupt announcement, the dialogue with the agent “Saki” isstill continued. If the user M taps a heart button 208 a displayed onthe screen 208, detailed information about the introduced partner isdisplayed (step S666). FIG. 44 illustrates an example of a screendisplaying detailed information about the introduced partner. In theillustrated example, on a screen 209, the profile of the introducedpartner (person P), a Want to Meet button 209 a, and a No Thanks button209 b are displayed (step S669).

Next, dialogue between the user and the person P through the agent isexecuted by chat or speech (step S672). The dialogue at this point isdialogue through the agent, in which the user does not yet hear theother person's voice directly, and instead utterances of the otherperson are output in the voice of the agent, or displayed as statementsof the agent. The user learns the profile of the other person for thefirst time in the introduction phase, but since the matching between theuser and the other person has been executed sufficiently by theprocesses up to the fixing phase, from the user side, the displayedprofile information about the person P is similar to the preferences ofthe agent that the user has been in contact with so far, and inaddition, the personality is also expected to be similar, thereby makingit possible to transition to conversation with the person P with thesame good impression. Also, from the person P (woman) side, since she isbeing introduced to a man who has already become good at conversationwith the agent who is a representation of herself, the probability ofliking the user is high. In this way, in the example, it becomespossible to make an introduction with an increased probability of asuccessful meeting.

Next, in the case in which either the user or the person P presses the“No Thanks” button (step S675/Yes), the matching with the person P iscanceled. In the case in which the user selects a different agent (stepS678/Yes), the agent learning section 353 removes the agent (step S681),and restarts the process from the agent selection (steps S413 to S437)of the search phase.

Alternatively, in the case in which the user does not select a differentagent (step S678/No), the process returns to the dialogue loop (stepsS440 to S446) in the search phase while keeping the current agent (inthe state of canceled matching with the person P), and matching isexecuted again (at this time, the person P whose matching has beencanceled may also be excluded for a fixed period).

On the other hand, in the case in which both press the “Want to Meet”button (step S684/Yes), in the case in which the user desires support(step S685), the support phase illustrated in FIG. 45 is started. In thecase in which support is not needed (step S685/Yes), the agent learningsection 353 executes an agent removal process (a process of removing theagent program launched on the client terminal 1) (step S688). At thistime, the user agent dialogue processing section 350 may also issue agreeting as described later with reference to FIG. 48 for the last timeto execute a dialogue control by which the agent leaves the user. Also,in the case in which the support phase is not needed, the user decides atime and place to meet, exchanges contact information, and the like bydialogue with the person P through the agent.

(4-3-5. Support Phase)

Next, an example of the support phase will be described with referenceto FIGS. 45 to 48. In the support phase, the user selects an arbitraryscenario (hereinafter also designated a “meeting plan”) from a list ofscenarios supporting a meeting registered in the scenario DB 359, and ameeting arrangement following the scenario is carried out by a relatedbusiness. The scenario may also be fee-based.

FIG. 45 is a flowchart illustrating an operating process in the supportphase according to the example. As illustrated in FIG. 45, first, theuser selects an arbitrary plan from among multiple meeting planspresented by the scenario management section 358 (step S693). Herein,FIG. 46 illustrates an example of a meeting plan selection screen. Inthe illustrated example, on a screen 210, detailed information about ameeting plan, such as the title, content, and expenses, as well as aSelect button 210 a and a Back button 210 b are displayed. The meetingplan has a title such as “A delightful surprise for her awaits at anelegant Italian restaurant”, for example, and as for the surprisecontent, content such as preparing a special menu, preparing a specialtable, or preparing a present is stated. The restaurant or facility thatappears in the meeting plan is tied in as an advertisement, for example,and even with a free meeting plan, an effect of attracting customers byguiding the user to the specific restaurant or facility may be expected.Additionally, a paid meeting plan may also be prepared to guide the userto a place that is unrelated to a sponsor.

When the user selects a meeting plan, the user and the person P arenotified of the time and place of the meeting plan (step S696). Herein,FIG. 47 illustrates an example of a meeting plan notification screen.The left side of FIG. 47 illustrates a screen 211 displayed on thedisplay section 106A of the client terminal 1 of the person P. On thescreen 211, a message from the user M such as “I made a reservationhere. I'm looking forward to meeting you!”, detailed information aboutthe meeting plan, an Accept button 211 a, and a Decline button 211 b aredisplayed. The person P checks the time, place, and the content of theplan, and taps the Accept button 211 a if there are no problems, or tapsthe Decline button 211 b in the case of declining the invitation.

Also, the right side of FIG. 47 illustrates a screen 212 displayed onthe display section 106B of the client terminal 1 of the user M. On thescreen 212, an indication that the person P has been invited, detailedinformation about the meeting plan, an OK button 212 a, and a Start Overbutton 212 b are displayed. The user M checks the time, place, and thecontent of the plan, and taps the OK button 212 a if there are noproblems, or taps the Start Over button 212 b in the case of changingthe time or plan.

Next, if both agree to the meeting plan, the scenario management section358 notifies a related business (such as a restaurant or facility) ofthe meeting plan (step S699).

Next, the flow waits until the specified time (step S702), and when thespecified time arrives, the scenario management section 358 checkswhether or not the two people have met at the specified time and place(step S705). Whether or not the two people have actually met may bedetermined automatically from position information about the two peopledetected by GPS or the like, or a notification of meeting may be issuedmanually from the user M.

Next, in the case in which the two people have met at the specified timeand place (step S705/Yes), the scenario management section 358 notifiesthe related business that the two people have met (step S708). Note thatin the case of a restaurant, it is possible to ascertain explicitly thatthe two people of a reservation have arrived. Subsequently, the relatedbusiness provides the content of the meeting plan to the two people.

Next, the scenario management section 358 waits for a completionnotification from the related business (step S711), and upon receivingthe completion notification, causes the agent to ask the user M if thetwo have decided to date (step S714). The timing of asking may also beafter a fixed period has elapsed.

Next, in the case of dating (step S714/Yes), the user agent dialogueprocessing section 350 controls the dialogue to express a congratulatorymessage and have the agent leave (step S717). Herein, FIG. 48illustrates an example of a final congratulatory message screen by theagent. In the illustrated example, on a screen 213, a message of theagent “Saki” asking the user M “Are you dating her?”, a “We're Dating”button 213 a, and a “It Didn't Work Out” button 213 b are displayed. Inthe case of dating the person P, the user M taps the “We're Dating”button 213 a. Subsequently, as illustrated on the right side of FIG. 48,on a screen 214, a final congratulatory message from the agent “Saki”such as “Oh . . . congratulations! I'm so happy for you. I mean, I'm acopy of her. My work here is done. You've come this far, so be happy. Ihave to go now. Goodbye.” is displayed.

Subsequently, the agent learning section 353 executes the agent removalprocess (the process of removing the agent application launched on theclient terminal 1) (step S720).

On the other hand, in the case in which the two people could not meet atthe specified time (step S723/No), the user agent dialogue processingsection 350 presents a message expressing disappointment from the agentto the user M (step S273).

Also, in the case of not dating (step S714/No), the user agent dialogueprocessing section 350 presents a message of consolation andencouragement from the agent to the user M (step S726).

Additionally, in the case in which the user selects a different agent(step S729/Yes), the agent learning section 353 removes the agent, andrestarts the process from the agent selection (steps S413 to S437) ofthe search phase.

Alternatively, in the case in which the user does not select a differentagent (step S729/No), the process returns to the dialogue loop (stepsS440 to S446) in the search phase while keeping the current agent (inthe state of canceled matching with the person P), and matching isexecuted again (at this time, the person P whose matching has beencanceled may also be excluded for a fixed period).

<<5. Conclusion>>

As described above, the communication control system according to anembodiment of the present disclosure is able to connect dialogue with anagent seamlessly to communication with a person in the real world.

The preferred embodiment(s) of the present disclosure has/have beendescribed above with reference to the accompanying drawings, whilst thepresent disclosure is not limited to the above examples. A personskilled in the art may find various alterations and modifications withinthe scope of the appended claims, and it should be understood that theywill naturally come under the technical scope of the present disclosure.

For example, it is also possible to create a computer program forcausing hardware such as a CPU, ROM, and RAM built into the clientterminal 1 or the agent server 2 described above to exhibit thefunctions of the client terminal 1 or the agent server 2. Also, acomputer-readable storage medium storing the computer program isprovided.

In addition, the embodiment described above illustrates a configurationin which various functions are realized by the agent server 2 on theInternet, but the present embodiment is not limited thereto, and atleast part of the configuration of the agent server 2 may also be in theclient terminal 1 (a smartphone, wearable terminal, or the like) of theuser. Also, the entire configuration of the agent server 2 may beprovided in the client terminal 1, and all processes may be executed onthe client terminal 1.

Also, although the example described above describes a case of assuminga male user and introducing a female marriage-seeking member, obviouslythe above is also applicable to a case of assuming a female user andintroducing a male marriage-seeking member.

Also, the technology is not limited to the matching of men and womenusing the marriage-seeking member information of a marriage consultingservice, and is also applicable to friend matching (either for the samesex or the opposite sex) on an SNS using information registered in theSNS, for example. Alternatively, the technology is also applicable tomatching between human resources and job seekers using job-seekinginformation.

In this way, the present embodiment is broadly applied to the matchingof persons in a variety of contexts.

Further, the effects described in this specification are merelyillustrative or exemplified effects, and are not limitative. That is,with or in the place of the above effects, the technology according tothe present disclosure may achieve other effects that are clear to thoseskilled in the art from the description of this specification.

Additionally, the present technology may also be configured as below.

(1)

An information processing system including:

a storage section that stores information about a plurality of agentscapable of dialogue with a user, each agent having different attributes;

a communication section that receives a message from the user from aclient terminal, and also replies to the client terminal with a responsemessage; and

a control section that executes control to

-   -   select a specific agent from the plurality of agents, according        to an instruction from the user,    -   record attributes of the specific agent updated according to        dialogue between the specific agent and the user as the        attributes of a user agent,    -   specify a partner user who most resembles the attributes of the        user agent by comparing the attributes of the user agent and        attributes of a plurality of actually existing partner users,        and    -   notify the user of the existence of the partner user at a        predetermined timing.        (2)

The information processing system according to (1), in which

the control section

-   -   records attributes of the specific agent updated to be closer to        attributes of the user as the attributes of the user agent, and    -   selects the partner user who most resembles the attributes of        the user agent on a fixed interval, and in a case in which a        same person is selected a predetermined number of times,        specifies the person as the partner user.        (3)

The information processing system according to (1) or (2), in which

the control section updates the attributes of the user agent accordingto the attributes of the partner user who most resembles the attributesof the user agent.

(4)

The information processing system according to any one of (2) to (3), inwhich

when the partner user who most resembles the attributes of the useragent is the same for a fixed period, the attributes of the user agentare updated according to the attributes of the partner user.

(5)

The information processing system according to (4), in which

the attributes of the plurality of actually existing partner users areacquired from an external apparatus.

(6)

The information processing system according to (4) or (5), in which

if a closeness between the user and the user agent exceeds a thresholdvalue, the control section notifies the user of the existence of thepartner user.

(7)

The information processing system according to (6), in which

the control section additionally notifies the partner user of theexistence of the user.

(8)

The information processing system according to (7), in which

if a request signal indicating a desire to meet is received from boththe user and the partner user, the control section becomes able toprovide a scenario of a meeting to the user.

(9)

The information processing system according to (8), in which

if a notification indicating a commencement of a relationship with thepartner user is received from the user, the control section executescontrol to transmit a congratulatory message to the user, and also toremove an agent application on the client terminal.

(10)

The information processing system according to any one of (1) to (9), inwhich

the attributes are at least one of profile information, preferenceinformation, personality information, ideal profile conditioninformation, and ideal personality information.

(11)

An information processing apparatus including:

a communication section that transmits a message from a user to a serverapparatus storing information about a plurality of agents capable ofdialogue with the user, each agent having different attributes, and alsoreceives a response message with respect to the message; and

a control section that executes control to

-   -   select a specific agent from the plurality of agents, according        to an instruction by the user, and    -   receive, from the server apparatus through the communication        section at a predetermined timing, a notification indicating an        existence of an actually existing partner user who most        resembles the attributes of a user agent obtained by updating        the attributes of the specific agent according to dialogue        between the specific agent and the user.        (12)

The information processing apparatus according to (11), in which

the control section receives, from the server apparatus, a notificationof the existence of the partner user at a timing when it is determinedthat a closeness between the user and the user agent exceeds a thresholdvalue.

(13)

The information processing apparatus according to (12), in which

the control section receives, from the server apparatus, a scenario of ameeting with the partner user.

(14)

The information processing apparatus according to (13), in which

the control section executes control to

-   -   transmit, to the server apparatus, a notification indicating a        commencement of a relationship with the partner user, according        to an instruction of the user, and    -   receive, from the server apparatus in response to the        notification indicating a commencement of a relationship, a        congratulatory message and a control signal with an instruction        for a removal of an agent application installed in the        information processing apparatus, and remove the agent        application according to the control signal.        (15)

The information processing apparatus according to any one of (11) to(14), in which

the attributes are at least one of profile information, preferenceinformation, personality information, ideal profile conditioninformation, and ideal personality information.

(16)

An information processing method, executed by a processor, including:

storing, in a storage section, information about a plurality of agentscapable of dialogue with a user, each agent having different attributes;

receiving a message from the user from a client terminal, and alsoreplying by a communication section to the client terminal with aresponse message; and

executing control to

-   -   select a specific agent from the plurality of agents, according        to an instruction from the user,    -   record attributes of the specific agent updated according to        dialogue between the specific agent and the user as the        attributes of a user agent,    -   specify a partner user who most resembles the attributes of the        user agent by comparing the attributes of the user agent and        attributes of a plurality of actually existing partner users,        and    -   notify the user of the existence of the partner user at a        predetermined timing.        (17)

A program causing a computer to function as:

a communication section that transmits a message from a user to a serverapparatus storing information about a plurality of agents capable ofdialogue with the user, each agent having different attributes, and alsoreceives a response message with respect to the message; and

a control section that executes control to

-   -   select a specific agent from the plurality of agents, according        to an instruction by the user, and    -   receive, from the server apparatus through the communication        section at a predetermined timing, a notification indicating an        existence of an actually existing partner user who most        resembles the attributes of a user agent obtained by updating        the attributes of the specific agent according to dialogue        between the specific agent and the user.

REFERENCE SIGNS LIST

-   1 client terminal-   2 agent server-   30 dialogue processing section-   300 dialogue processing section-   310 question search section-   320 response generation section-   330 conversation DB-   340 phoneme data acquisition section-   30 a dialogue processing section-   31 basic dialogue processing section-   32 character A dialogue processing section-   33 person B dialogue processing section-   34 person C dialogue processing section-   35 matching section-   350 user agent dialogue processing section-   351 user/agent information management section 351-   352 user information DB-   353 agent learning section-   354 user agent DB-   355 user-to-introduce selection section-   356 closeness computation section-   357 introduction processing section-   358 scenario management section-   359 scenario DB-   360 agent information DB-   36 communication section-   40 phoneme storage section-   41 basic phoneme DB-   42 character A phoneme DB-   43 person B phoneme DB-   44 person C phoneme DB-   50 conversation DB generation section-   60 phoneme DB generation section-   70 advertisement insertion processing section-   72 advertisement DB-   80 feedback acquisition processing section-   3 network-   10 agent

1. An information processing system comprising: a storage section thatstores information about a plurality of agents capable of dialogue witha user, each agent having different attributes; a communication sectionthat receives a message from the user from a client terminal, and alsoreplies to the client terminal with a response message; and a controlsection that executes control to select a specific agent from theplurality of agents, according to an instruction from the user, recordattributes of the specific agent updated according to dialogue betweenthe specific agent and the user as the attributes of a user agent,specify a partner user who most resembles the attributes of the useragent by comparing the attributes of the user agent and attributes of aplurality of actually existing partner users, and notify the user of theexistence of the partner user at a predetermined timing.
 2. Theinformation processing system according to claim 1, wherein the controlsection records attributes of the specific agent updated to be closer toattributes of the user as the attributes of the user agent, and selectsthe partner user who most resembles the attributes of the user agent ona fixed interval, and in a case in which a same person is selected apredetermined number of times, specifies the person as the partner user.3. The information processing system according to claim 1, wherein thecontrol section updates the attributes of the user agent according tothe attributes of the partner user who most resembles the attributes ofthe user agent.
 4. The information processing system according to claim2, wherein when the partner user who most resembles the attributes ofthe user agent is the same for a fixed period, the attributes of theuser agent are updated according to the attributes of the partner user.5. The information processing system according to claim 4, wherein theattributes of the plurality of actually existing partner users areacquired from an external apparatus.
 6. The information processingsystem according to claim 4, wherein if a closeness between the user andthe user agent exceeds a threshold value, the control section notifiesthe user of the existence of the partner user.
 7. The informationprocessing system according to claim 6, wherein the control sectionadditionally notifies the partner user of the existence of the user. 8.The information processing system according to claim 7, wherein if arequest signal indicating a desire to meet is received from both theuser and the partner user, the control section becomes able to provide ascenario of a meeting to the user.
 9. The information processing systemaccording to claim 8, wherein if a notification indicating acommencement of a relationship with the partner user is received fromthe user, the control section executes control to transmit acongratulatory message to the user, and also to remove an agentapplication on the client terminal.
 10. The information processingsystem according to claim 1, wherein the attributes are at least one ofprofile information, preference information, personality information,ideal profile condition information, and ideal personality information.11. An information processing apparatus comprising: a communicationsection that transmits a message from a user to a server apparatusstoring information about a plurality of agents capable of dialogue withthe user, each agent having different attributes, and also receives aresponse message with respect to the message; and a control section thatexecutes control to select a specific agent from the plurality ofagents, according to an instruction by the user, and receive, from theserver apparatus through the communication section at a predeterminedtiming, a notification indicating an existence of an actually existingpartner user who most resembles the attributes of a user agent obtainedby updating the attributes of the specific agent according to dialoguebetween the specific agent and the user.
 12. The information processingapparatus according to claim 11, wherein the control section receives,from the server apparatus, a notification of the existence of thepartner user at a timing when it is determined that a closeness betweenthe user and the user agent exceeds a threshold value.
 13. Theinformation processing apparatus according to claim 12, wherein thecontrol section receives, from the server apparatus, a scenario of ameeting with the partner user.
 14. The information processing apparatusaccording to claim 13, wherein the control section executes control totransmit, to the server apparatus, a notification indicating acommencement of a relationship with the partner user, according to aninstruction of the user, and receive, from the server apparatus inresponse to the notification indicating a commencement of arelationship, a congratulatory message and a control signal with aninstruction for a removal of an agent application installed in theinformation processing apparatus, and remove the agent applicationaccording to the control signal.
 15. The information processingapparatus according to claim 11, wherein the attributes are at least oneof profile information, preference information, personality information,ideal profile condition information, and ideal personality information.16. An information processing method, executed by a processor,comprising: storing, in a storage section, information about a pluralityof agents capable of dialogue with a user, each agent having differentattributes; receiving a message from the user from a client terminal,and also replying by a communication section to the client terminal witha response message; and executing control to select a specific agentfrom the plurality of agents, according to an instruction from the user,record attributes of the specific agent updated according to dialoguebetween the specific agent and the user as the attributes of a useragent, specify a partner user who most resembles the attributes of theuser agent by comparing the attributes of the user agent and attributesof a plurality of actually existing partner users, and notify the userof the existence of the partner user at a predetermined timing.
 17. Aprogram causing a computer to function as: a communication section thattransmits a message from a user to a server apparatus storinginformation about a plurality of agents capable of dialogue with theuser, each agent having different attributes, and also receives aresponse message with respect to the message; and a control section thatexecutes control to select a specific agent from the plurality ofagents, according to an instruction by the user, and receive, from theserver apparatus through the communication section at a predeterminedtiming, a notification indicating an existence of an actually existingpartner user who most resembles the attributes of a user agent obtainedby updating the attributes of the specific agent according to dialoguebetween the specific agent and the user.